{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTYTwhGW1rZS",
        "outputId": "773f8d42-9a0f-4930-abee-3585e8beaa83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# connect to drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulPH65OC3gbh"
      },
      "outputs": [],
      "source": [
        "# import\n",
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "\n",
        "# libraries\n",
        "from datetime import datetime\n",
        "import re\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "from datetime import datetime, timedelta\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.api import VAR\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as pdr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-yxFdjSl2W-u"
      },
      "outputs": [],
      "source": [
        "# read data from google drive\n",
        "#data = pd.read_excel('/content/drive/MyDrive/Rady_for_R_GBPUSD.xlsx', parse_dates =['Date'])\n",
        "data = pd.read_excel('/content/drive/MyDrive/Rady_for_R_GBPUSD_EPU.xlsx', parse_dates =['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R0fAcGOg4LBv",
        "outputId": "58e447ca-ea7b-4d05-e890-7de47d42c5fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date    USDGBP  CPI_US  CPI_UK  Money Market Rate_US  \\\n",
              "0 2000-01-01  1.639417   164.1  71.936                0.0604   \n",
              "1 2000-02-01  1.599864   164.4  72.159                0.0610   \n",
              "2 2000-03-01  1.580220   164.7  72.338                0.0620   \n",
              "3 2000-04-01  1.583365   164.7  72.573                0.0631   \n",
              "4 2000-05-01  1.508717   164.8  72.771                0.0676   \n",
              "\n",
              "   Money Market Rate_UK   IPI_US  IPI_UK   M2_US     M2_UK  Stock Market News  \\\n",
              "0                0.0614  94.5458   112.5  4656.2  890517.0           0.258237   \n",
              "1                0.0624  94.8185   113.7  4669.4  873293.0           0.259493   \n",
              "2                0.0623  95.1983   113.6  4699.9  887465.0           0.262109   \n",
              "3                0.0630  95.8921   113.2  4755.9  870743.0           0.259771   \n",
              "4                0.0630  96.0691   113.6  4743.9  845815.0           0.257433   \n",
              "\n",
              "   Economic Development News  FED News  Micro Finance News  \\\n",
              "0                   0.265871  0.255893            0.249303   \n",
              "1                   0.261406  0.255016            0.252066   \n",
              "2                   0.258737  0.254687            0.247164   \n",
              "3                   0.263898  0.254085            0.248209   \n",
              "4                   0.269059  0.253482            0.249254   \n",
              "\n",
              "   International Trade News      EPU_US      EPU_UK  \n",
              "0                  0.260273  187.453420   53.748451  \n",
              "1                  0.260401  108.823551   58.396511  \n",
              "2                  0.265140   58.469634   61.960393  \n",
              "3                  0.262308   92.103565  114.010429  \n",
              "4                  0.259477  181.052572   46.368325  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>USDGBP</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
              "      <th>IPI_US</th>\n",
              "      <th>IPI_UK</th>\n",
              "      <th>M2_US</th>\n",
              "      <th>M2_UK</th>\n",
              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
              "      <th>Micro Finance News</th>\n",
              "      <th>International Trade News</th>\n",
              "      <th>EPU_US</th>\n",
              "      <th>EPU_UK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-01-01</td>\n",
              "      <td>1.639417</td>\n",
              "      <td>164.1</td>\n",
              "      <td>71.936</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>0.0614</td>\n",
              "      <td>94.5458</td>\n",
              "      <td>112.5</td>\n",
              "      <td>4656.2</td>\n",
              "      <td>890517.0</td>\n",
              "      <td>0.258237</td>\n",
              "      <td>0.265871</td>\n",
              "      <td>0.255893</td>\n",
              "      <td>0.249303</td>\n",
              "      <td>0.260273</td>\n",
              "      <td>187.453420</td>\n",
              "      <td>53.748451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-02-01</td>\n",
              "      <td>1.599864</td>\n",
              "      <td>164.4</td>\n",
              "      <td>72.159</td>\n",
              "      <td>0.0610</td>\n",
              "      <td>0.0624</td>\n",
              "      <td>94.8185</td>\n",
              "      <td>113.7</td>\n",
              "      <td>4669.4</td>\n",
              "      <td>873293.0</td>\n",
              "      <td>0.259493</td>\n",
              "      <td>0.261406</td>\n",
              "      <td>0.255016</td>\n",
              "      <td>0.252066</td>\n",
              "      <td>0.260401</td>\n",
              "      <td>108.823551</td>\n",
              "      <td>58.396511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000-03-01</td>\n",
              "      <td>1.580220</td>\n",
              "      <td>164.7</td>\n",
              "      <td>72.338</td>\n",
              "      <td>0.0620</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>95.1983</td>\n",
              "      <td>113.6</td>\n",
              "      <td>4699.9</td>\n",
              "      <td>887465.0</td>\n",
              "      <td>0.262109</td>\n",
              "      <td>0.258737</td>\n",
              "      <td>0.254687</td>\n",
              "      <td>0.247164</td>\n",
              "      <td>0.265140</td>\n",
              "      <td>58.469634</td>\n",
              "      <td>61.960393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-04-01</td>\n",
              "      <td>1.583365</td>\n",
              "      <td>164.7</td>\n",
              "      <td>72.573</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.0630</td>\n",
              "      <td>95.8921</td>\n",
              "      <td>113.2</td>\n",
              "      <td>4755.9</td>\n",
              "      <td>870743.0</td>\n",
              "      <td>0.259771</td>\n",
              "      <td>0.263898</td>\n",
              "      <td>0.254085</td>\n",
              "      <td>0.248209</td>\n",
              "      <td>0.262308</td>\n",
              "      <td>92.103565</td>\n",
              "      <td>114.010429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-05-01</td>\n",
              "      <td>1.508717</td>\n",
              "      <td>164.8</td>\n",
              "      <td>72.771</td>\n",
              "      <td>0.0676</td>\n",
              "      <td>0.0630</td>\n",
              "      <td>96.0691</td>\n",
              "      <td>113.6</td>\n",
              "      <td>4743.9</td>\n",
              "      <td>845815.0</td>\n",
              "      <td>0.257433</td>\n",
              "      <td>0.269059</td>\n",
              "      <td>0.253482</td>\n",
              "      <td>0.249254</td>\n",
              "      <td>0.259477</td>\n",
              "      <td>181.052572</td>\n",
              "      <td>46.368325</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-9d68a844-1763-47d1-8ac1-3c9a5a8af73b button');\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztN6xB0WBFU1"
      },
      "outputs": [],
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data['USDGBP'] = 1/data['USDGBP']\n",
        "data.rename(columns={'USDGBP':'GBPUSD'}, inplace=True)\n",
        "#data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "e-ebZQLCCXc8",
        "outputId": "39d4c9a3-805c-4a9f-906c-658ad638b6c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GBPUSD   CPI_US   CPI_UK  Money Market Rate_US  \\\n",
              "Date                                                           \n",
              "2000-01-01  0.609973  164.100   71.936                0.0604   \n",
              "2000-02-01  0.625053  164.400   72.159                0.0610   \n",
              "2000-03-01  0.632823  164.700   72.338                0.0620   \n",
              "2000-04-01  0.631566  164.700   72.573                0.0631   \n",
              "2000-05-01  0.662815  164.800   72.771                0.0676   \n",
              "...              ...      ...      ...                   ...   \n",
              "2017-04-01  0.791045  237.808  102.949                0.0116   \n",
              "2017-05-01  0.773478  238.078  103.309                0.0119   \n",
              "2017-06-01  0.780442  238.908  103.284                0.0126   \n",
              "2017-07-01  0.769593  239.362  103.215                0.0131   \n",
              "2017-08-01  0.771999  239.842  103.785                0.0131   \n",
              "\n",
              "            Money Market Rate_UK    IPI_US  IPI_UK    M2_US      M2_UK  \\\n",
              "Date                                                                     \n",
              "2000-01-01                0.0614   94.5458   112.5   4656.2   890517.0   \n",
              "2000-02-01                0.0624   94.8185   113.7   4669.4   873293.0   \n",
              "2000-03-01                0.0623   95.1983   113.6   4699.9   887465.0   \n",
              "2000-04-01                0.0630   95.8921   113.2   4755.9   870743.0   \n",
              "2000-05-01                0.0630   96.0691   113.6   4743.9   845815.0   \n",
              "...                          ...       ...     ...      ...        ...   \n",
              "2017-04-01                0.0033  105.0468   101.8  13452.7  2133785.0   \n",
              "2017-05-01                0.0031  105.0136   102.0  13517.8  2128826.0   \n",
              "2017-06-01                0.0029  105.2469   102.4  13544.0  2161166.0   \n",
              "2017-07-01                0.0029  105.1038   102.8  13620.4  2195459.0   \n",
              "2017-08-01                0.0028  104.3403   102.9  13664.4  2150923.0   \n",
              "\n",
              "            Stock Market News  Economic Development News  FED News  \\\n",
              "Date                                                                 \n",
              "2000-01-01           0.258237                   0.265871  0.255893   \n",
              "2000-02-01           0.259493                   0.261406  0.255016   \n",
              "2000-03-01           0.262109                   0.258737  0.254687   \n",
              "2000-04-01           0.259771                   0.263898  0.254085   \n",
              "2000-05-01           0.257433                   0.269059  0.253482   \n",
              "...                       ...                        ...       ...   \n",
              "2017-04-01           0.255181                   0.257543  0.260854   \n",
              "2017-05-01           0.255313                   0.251881  0.256059   \n",
              "2017-06-01           0.255768                   0.259531  0.264680   \n",
              "2017-07-01           0.255551                   0.258921  0.262572   \n",
              "2017-08-01           0.254705                   0.256595  0.260185   \n",
              "\n",
              "            Micro Finance News  International Trade News      EPU_US  \\\n",
              "Date                                                                   \n",
              "2000-01-01            0.249303                  0.260273  187.453420   \n",
              "2000-02-01            0.252066                  0.260401  108.823551   \n",
              "2000-03-01            0.247164                  0.265140   58.469634   \n",
              "2000-04-01            0.248209                  0.262308   92.103565   \n",
              "2000-05-01            0.249254                  0.259477  181.052572   \n",
              "...                        ...                       ...         ...   \n",
              "2017-04-01            0.259239                  0.260659  535.702312   \n",
              "2017-05-01            0.265749                  0.254874  370.092425   \n",
              "2017-06-01            0.256290                  0.257576  445.578935   \n",
              "2017-07-01            0.259913                  0.257409  451.197292   \n",
              "2017-08-01            0.264162                  0.254985  402.873332   \n",
              "\n",
              "                EPU_UK  \n",
              "Date                    \n",
              "2000-01-01   53.748451  \n",
              "2000-02-01   58.396511  \n",
              "2000-03-01   61.960393  \n",
              "2000-04-01  114.010429  \n",
              "2000-05-01   46.368325  \n",
              "...                ...  \n",
              "2017-04-01  169.176036  \n",
              "2017-05-01  159.401445  \n",
              "2017-06-01  318.991160  \n",
              "2017-07-01  183.658951  \n",
              "2017-08-01  151.204298  \n",
              "\n",
              "[212 rows x 16 columns]"
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              "      <th>GBPUSD</th>\n",
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              "      <th>Date</th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2000-01-01</th>\n",
              "      <td>0.609973</td>\n",
              "      <td>164.100</td>\n",
              "      <td>71.936</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>0.0614</td>\n",
              "      <td>94.5458</td>\n",
              "      <td>112.5</td>\n",
              "      <td>4656.2</td>\n",
              "      <td>890517.0</td>\n",
              "      <td>0.258237</td>\n",
              "      <td>0.265871</td>\n",
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              "      <td>187.453420</td>\n",
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              "      <th>2000-02-01</th>\n",
              "      <td>0.625053</td>\n",
              "      <td>164.400</td>\n",
              "      <td>72.159</td>\n",
              "      <td>0.0610</td>\n",
              "      <td>0.0624</td>\n",
              "      <td>94.8185</td>\n",
              "      <td>113.7</td>\n",
              "      <td>4669.4</td>\n",
              "      <td>873293.0</td>\n",
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              "      <td>108.823551</td>\n",
              "      <td>58.396511</td>\n",
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              "      <th>2000-03-01</th>\n",
              "      <td>0.632823</td>\n",
              "      <td>164.700</td>\n",
              "      <td>72.338</td>\n",
              "      <td>0.0620</td>\n",
              "      <td>0.0623</td>\n",
              "      <td>95.1983</td>\n",
              "      <td>113.6</td>\n",
              "      <td>4699.9</td>\n",
              "      <td>887465.0</td>\n",
              "      <td>0.262109</td>\n",
              "      <td>0.258737</td>\n",
              "      <td>0.254687</td>\n",
              "      <td>0.247164</td>\n",
              "      <td>0.265140</td>\n",
              "      <td>58.469634</td>\n",
              "      <td>61.960393</td>\n",
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              "      <th>2000-04-01</th>\n",
              "      <td>0.631566</td>\n",
              "      <td>164.700</td>\n",
              "      <td>72.573</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.0630</td>\n",
              "      <td>95.8921</td>\n",
              "      <td>113.2</td>\n",
              "      <td>4755.9</td>\n",
              "      <td>870743.0</td>\n",
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              "      <td>0.263898</td>\n",
              "      <td>0.254085</td>\n",
              "      <td>0.248209</td>\n",
              "      <td>0.262308</td>\n",
              "      <td>92.103565</td>\n",
              "      <td>114.010429</td>\n",
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              "      <th>2000-05-01</th>\n",
              "      <td>0.662815</td>\n",
              "      <td>164.800</td>\n",
              "      <td>72.771</td>\n",
              "      <td>0.0676</td>\n",
              "      <td>0.0630</td>\n",
              "      <td>96.0691</td>\n",
              "      <td>113.6</td>\n",
              "      <td>4743.9</td>\n",
              "      <td>845815.0</td>\n",
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              "      <td>0.269059</td>\n",
              "      <td>0.253482</td>\n",
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              "      <td>0.259477</td>\n",
              "      <td>181.052572</td>\n",
              "      <td>46.368325</td>\n",
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              "      <th>2017-04-01</th>\n",
              "      <td>0.791045</td>\n",
              "      <td>237.808</td>\n",
              "      <td>102.949</td>\n",
              "      <td>0.0116</td>\n",
              "      <td>0.0033</td>\n",
              "      <td>105.0468</td>\n",
              "      <td>101.8</td>\n",
              "      <td>13452.7</td>\n",
              "      <td>2133785.0</td>\n",
              "      <td>0.255181</td>\n",
              "      <td>0.257543</td>\n",
              "      <td>0.260854</td>\n",
              "      <td>0.259239</td>\n",
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              "      <td>169.176036</td>\n",
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              "      <th>2017-05-01</th>\n",
              "      <td>0.773478</td>\n",
              "      <td>238.078</td>\n",
              "      <td>103.309</td>\n",
              "      <td>0.0119</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>105.0136</td>\n",
              "      <td>102.0</td>\n",
              "      <td>13517.8</td>\n",
              "      <td>2128826.0</td>\n",
              "      <td>0.255313</td>\n",
              "      <td>0.251881</td>\n",
              "      <td>0.256059</td>\n",
              "      <td>0.265749</td>\n",
              "      <td>0.254874</td>\n",
              "      <td>370.092425</td>\n",
              "      <td>159.401445</td>\n",
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              "      <th>2017-06-01</th>\n",
              "      <td>0.780442</td>\n",
              "      <td>238.908</td>\n",
              "      <td>103.284</td>\n",
              "      <td>0.0126</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>105.2469</td>\n",
              "      <td>102.4</td>\n",
              "      <td>13544.0</td>\n",
              "      <td>2161166.0</td>\n",
              "      <td>0.255768</td>\n",
              "      <td>0.259531</td>\n",
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              "      <td>0.257576</td>\n",
              "      <td>445.578935</td>\n",
              "      <td>318.991160</td>\n",
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              "      <th>2017-07-01</th>\n",
              "      <td>0.769593</td>\n",
              "      <td>239.362</td>\n",
              "      <td>103.215</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>105.1038</td>\n",
              "      <td>102.8</td>\n",
              "      <td>13620.4</td>\n",
              "      <td>2195459.0</td>\n",
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              "      <td>183.658951</td>\n",
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              "      <th>2017-08-01</th>\n",
              "      <td>0.771999</td>\n",
              "      <td>239.842</td>\n",
              "      <td>103.785</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.0028</td>\n",
              "      <td>104.3403</td>\n",
              "      <td>102.9</td>\n",
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              "      <td>2150923.0</td>\n",
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              "      <td>402.873332</td>\n",
              "      <td>151.204298</td>\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-536b0a20-72f2-461e-98f3-89d3e73b37ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#Declear that it is a time series data\n",
        "data.index = data.Date\n",
        "data = data.drop('Date', axis=1)\n",
        "#data.drop('M2_US', axis=1, inplace=True)\n",
        "#data.drop('M2_UK', axis=1, inplace=True)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GUHXZ2VIDCGb",
        "outputId": "30108d75-860e-4acc-e46e-074d053acab5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           GBPUSD      CPI_US      CPI_UK  Money Market Rate_US  \\\n",
              "count  212.000000  212.000000  212.000000            212.000000   \n",
              "mean     0.624364  205.388042   86.634476              0.020400   \n",
              "std      0.072773   23.659320   10.400193              0.020266   \n",
              "min      0.483073  164.100000   71.936000              0.002300   \n",
              "25%      0.570982  183.175000   76.552000              0.003175   \n",
              "50%      0.630161  208.107000   85.643000              0.011650   \n",
              "75%      0.665188  227.963750   98.175000              0.033100   \n",
              "max      0.811079  239.842000  103.785000              0.067900   \n",
              "\n",
              "       Money Market Rate_UK      IPI_US      IPI_UK         M2_US  \\\n",
              "count            212.000000  212.000000  212.000000    212.000000   \n",
              "mean               0.028726   98.724471  105.151415   8404.090566   \n",
              "std                0.022490    4.899083    5.695325   2587.577509   \n",
              "min                0.002800   87.065000   95.200000   4656.200000   \n",
              "25%                0.005875   94.750325   99.700000   6255.900000   \n",
              "50%                0.035050   99.569900  104.750000   7976.800000   \n",
              "75%                0.048900  103.119575  110.400000  10557.225000   \n",
              "max                0.066500  106.613400  114.300000  13664.400000   \n",
              "\n",
              "              M2_UK  Stock Market News  Economic Development News    FED News  \\\n",
              "count  2.120000e+02         212.000000                 212.000000  212.000000   \n",
              "mean   1.578439e+06           0.257548                   0.258559    0.258019   \n",
              "std    7.104018e+05           0.002908                   0.002818    0.003253   \n",
              "min    8.472600e+02           0.248426                   0.251881    0.249076   \n",
              "25%    1.072978e+06           0.255798                   0.256659    0.255679   \n",
              "50%    1.845732e+06           0.257151                   0.258310    0.258170   \n",
              "75%    2.094373e+06           0.258905                   0.260234    0.260513   \n",
              "max    2.498761e+06           0.274266                   0.269059    0.265534   \n",
              "\n",
              "       Micro Finance News  International Trade News      EPU_US      EPU_UK  \n",
              "count          212.000000                212.000000  212.000000  212.000000  \n",
              "mean             0.257254                  0.258634  161.584631  117.236699  \n",
              "std              0.005645                  0.003297  102.076036   70.295937  \n",
              "min              0.244891                  0.250209   31.958258   24.035942  \n",
              "25%              0.252988                  0.256069   91.515842   67.992585  \n",
              "50%              0.256817                  0.257922  137.057354  108.271939  \n",
              "75%              0.260831                  0.260585  198.666496  145.853518  \n",
              "max              0.272551                  0.268980  692.238702  558.223642  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37dd3e4a-c119-4aee-b0e5-01a26b6dfe80\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GBPUSD</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
              "      <th>IPI_US</th>\n",
              "      <th>IPI_UK</th>\n",
              "      <th>M2_US</th>\n",
              "      <th>M2_UK</th>\n",
              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
              "      <th>Micro Finance News</th>\n",
              "      <th>International Trade News</th>\n",
              "      <th>EPU_US</th>\n",
              "      <th>EPU_UK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>2.120000e+02</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>212.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.624364</td>\n",
              "      <td>205.388042</td>\n",
              "      <td>86.634476</td>\n",
              "      <td>0.020400</td>\n",
              "      <td>0.028726</td>\n",
              "      <td>98.724471</td>\n",
              "      <td>105.151415</td>\n",
              "      <td>8404.090566</td>\n",
              "      <td>1.578439e+06</td>\n",
              "      <td>0.257548</td>\n",
              "      <td>0.258559</td>\n",
              "      <td>0.258019</td>\n",
              "      <td>0.257254</td>\n",
              "      <td>0.258634</td>\n",
              "      <td>161.584631</td>\n",
              "      <td>117.236699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.072773</td>\n",
              "      <td>23.659320</td>\n",
              "      <td>10.400193</td>\n",
              "      <td>0.020266</td>\n",
              "      <td>0.022490</td>\n",
              "      <td>4.899083</td>\n",
              "      <td>5.695325</td>\n",
              "      <td>2587.577509</td>\n",
              "      <td>7.104018e+05</td>\n",
              "      <td>0.002908</td>\n",
              "      <td>0.002818</td>\n",
              "      <td>0.003253</td>\n",
              "      <td>0.005645</td>\n",
              "      <td>0.003297</td>\n",
              "      <td>102.076036</td>\n",
              "      <td>70.295937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.483073</td>\n",
              "      <td>164.100000</td>\n",
              "      <td>71.936000</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>0.002800</td>\n",
              "      <td>87.065000</td>\n",
              "      <td>95.200000</td>\n",
              "      <td>4656.200000</td>\n",
              "      <td>8.472600e+02</td>\n",
              "      <td>0.248426</td>\n",
              "      <td>0.251881</td>\n",
              "      <td>0.249076</td>\n",
              "      <td>0.244891</td>\n",
              "      <td>0.250209</td>\n",
              "      <td>31.958258</td>\n",
              "      <td>24.035942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.570982</td>\n",
              "      <td>183.175000</td>\n",
              "      <td>76.552000</td>\n",
              "      <td>0.003175</td>\n",
              "      <td>0.005875</td>\n",
              "      <td>94.750325</td>\n",
              "      <td>99.700000</td>\n",
              "      <td>6255.900000</td>\n",
              "      <td>1.072978e+06</td>\n",
              "      <td>0.255798</td>\n",
              "      <td>0.256659</td>\n",
              "      <td>0.255679</td>\n",
              "      <td>0.252988</td>\n",
              "      <td>0.256069</td>\n",
              "      <td>91.515842</td>\n",
              "      <td>67.992585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.630161</td>\n",
              "      <td>208.107000</td>\n",
              "      <td>85.643000</td>\n",
              "      <td>0.011650</td>\n",
              "      <td>0.035050</td>\n",
              "      <td>99.569900</td>\n",
              "      <td>104.750000</td>\n",
              "      <td>7976.800000</td>\n",
              "      <td>1.845732e+06</td>\n",
              "      <td>0.257151</td>\n",
              "      <td>0.258310</td>\n",
              "      <td>0.258170</td>\n",
              "      <td>0.256817</td>\n",
              "      <td>0.257922</td>\n",
              "      <td>137.057354</td>\n",
              "      <td>108.271939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.665188</td>\n",
              "      <td>227.963750</td>\n",
              "      <td>98.175000</td>\n",
              "      <td>0.033100</td>\n",
              "      <td>0.048900</td>\n",
              "      <td>103.119575</td>\n",
              "      <td>110.400000</td>\n",
              "      <td>10557.225000</td>\n",
              "      <td>2.094373e+06</td>\n",
              "      <td>0.258905</td>\n",
              "      <td>0.260234</td>\n",
              "      <td>0.260513</td>\n",
              "      <td>0.260831</td>\n",
              "      <td>0.260585</td>\n",
              "      <td>198.666496</td>\n",
              "      <td>145.853518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.811079</td>\n",
              "      <td>239.842000</td>\n",
              "      <td>103.785000</td>\n",
              "      <td>0.067900</td>\n",
              "      <td>0.066500</td>\n",
              "      <td>106.613400</td>\n",
              "      <td>114.300000</td>\n",
              "      <td>13664.400000</td>\n",
              "      <td>2.498761e+06</td>\n",
              "      <td>0.274266</td>\n",
              "      <td>0.269059</td>\n",
              "      <td>0.265534</td>\n",
              "      <td>0.272551</td>\n",
              "      <td>0.268980</td>\n",
              "      <td>692.238702</td>\n",
              "      <td>558.223642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37dd3e4a-c119-4aee-b0e5-01a26b6dfe80')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-37dd3e4a-c119-4aee-b0e5-01a26b6dfe80 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-37dd3e4a-c119-4aee-b0e5-01a26b6dfe80');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-caa32cc1-5604-4fe8-bee0-40f3fa003134\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-caa32cc1-5604-4fe8-bee0-40f3fa003134')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-caa32cc1-5604-4fe8-bee0-40f3fa003134 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        " data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E6G271IkrFn_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d027d515-c09c-40cd-dfe1-9f6a2aab8aef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/stattools.py:1488: FutureWarning: verbose is deprecated since functions should not print results\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "maxlag=7 #becuase we got this value before. We are not suppose to add 1 to it\n",
        "test = 'ssr_chi2test'\n",
        "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):\n",
        "    \"\"\"Check Granger Causality of all possible combinations of the Time series.\n",
        "    The rows are the response variable, columns are predictors. The values in the table\n",
        "    are the P-Values. P-Values lesser than the significance level (0.05), implies\n",
        "    the Null Hypothesis that the coefficients of the corresponding past values is\n",
        "    zero, that is, the X does not cause Y can be rejected.\n",
        "\n",
        "    data      : pandas dataframe containing the time series variables\n",
        "    variables : list containing names of the time series variables.\n",
        "    \"\"\"\n",
        "    df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
        "    for c in df.columns:\n",
        "        for r in df.index:\n",
        "            test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n",
        "            p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n",
        "            if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n",
        "            min_p_value = np.min(p_values)\n",
        "            df.loc[r, c] = min_p_value\n",
        "    df.columns = [var + '_x' for var in variables]\n",
        "    df.index = [var + '_y' for var in variables]\n",
        "    return df\n",
        "granger = grangers_causation_matrix(data, variables = data.columns)\n",
        "#grangers_causation_matrix(data, variables = data.columns)\n",
        "#print(granger.to_latex())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aY7ye9IlkUCI"
      },
      "outputs": [],
      "source": [
        "#granger"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5T2DsAbCLnW",
        "outputId": "d5095371-dc65-49c7-f5ed-64f80c2db2a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 1\n",
            "ssr based F test:         F=0.9079  , p=0.3418  , df_denom=208, df_num=1\n",
            "ssr based chi2 test:   chi2=0.9210  , p=0.3372  , df=1\n",
            "likelihood ratio test: chi2=0.9190  , p=0.3377  , df=1\n",
            "parameter F test:         F=0.9079  , p=0.3418  , df_denom=208, df_num=1\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 2\n",
            "ssr based F test:         F=0.4696  , p=0.6259  , df_denom=205, df_num=2\n",
            "ssr based chi2 test:   chi2=0.9620  , p=0.6182  , df=2\n",
            "likelihood ratio test: chi2=0.9598  , p=0.6188  , df=2\n",
            "parameter F test:         F=0.4696  , p=0.6259  , df_denom=205, df_num=2\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 3\n",
            "ssr based F test:         F=1.0980  , p=0.3511  , df_denom=202, df_num=3\n",
            "ssr based chi2 test:   chi2=3.4081  , p=0.3329  , df=3\n",
            "likelihood ratio test: chi2=3.3806  , p=0.3366  , df=3\n",
            "parameter F test:         F=1.0980  , p=0.3511  , df_denom=202, df_num=3\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 4\n",
            "ssr based F test:         F=0.7645  , p=0.5495  , df_denom=199, df_num=4\n",
            "ssr based chi2 test:   chi2=3.1965  , p=0.5255  , df=4\n",
            "likelihood ratio test: chi2=3.1722  , p=0.5294  , df=4\n",
            "parameter F test:         F=0.7645  , p=0.5495  , df_denom=199, df_num=4\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 5\n",
            "ssr based F test:         F=0.8414  , p=0.5218  , df_denom=196, df_num=5\n",
            "ssr based chi2 test:   chi2=4.4430  , p=0.4875  , df=5\n",
            "likelihood ratio test: chi2=4.3960  , p=0.4939  , df=5\n",
            "parameter F test:         F=0.8414  , p=0.5218  , df_denom=196, df_num=5\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 6\n",
            "ssr based F test:         F=0.6607  , p=0.6815  , df_denom=193, df_num=6\n",
            "ssr based chi2 test:   chi2=4.2314  , p=0.6454  , df=6\n",
            "likelihood ratio test: chi2=4.1885  , p=0.6512  , df=6\n",
            "parameter F test:         F=0.6607  , p=0.6815  , df_denom=193, df_num=6\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 7\n",
            "ssr based F test:         F=0.6867  , p=0.6832  , df_denom=190, df_num=7\n",
            "ssr based chi2 test:   chi2=5.1865  , p=0.6372  , df=7\n",
            "likelihood ratio test: chi2=5.1220  , p=0.6451  , df=7\n",
            "parameter F test:         F=0.6867  , p=0.6832  , df_denom=190, df_num=7\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: ({'ssr_ftest': (0.9078729231056208, 0.3417853647237551, 208.0, 1),\n",
              "   'ssr_chi2test': (0.9209672441119519, 0.33722113365843853, 1),\n",
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              "   'lrtest': (0.9598417826516652, 0.6188323448761441, 2),\n",
              "   'params_ftest': (0.46956650393538785, 0.6259441034780673, 205.0, 2.0)},\n",
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              "   array([[0., 0., 1., 0., 0.],\n",
              "          [0., 0., 0., 1., 0.]])]),\n",
              " 3: ({'ssr_ftest': (1.0979707085694295, 0.351080588720349, 202.0, 3),\n",
              "   'ssr_chi2test': (3.408057595411051, 0.33288397257705865, 3),\n",
              "   'lrtest': (3.380569280022655, 0.3365854019938785, 3),\n",
              "   'params_ftest': (1.0979707085696402, 0.35108058872026016, 202.0, 3.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bef289a0>,\n",
              "   <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bef2baf0>,\n",
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              "          [0., 0., 0., 0., 1., 0., 0.],\n",
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              " 4: ({'ssr_ftest': (0.7645418744921132, 0.54947652236154, 199.0, 4),\n",
              "   'ssr_chi2test': (3.1964765807911464, 0.5255002275512249, 4),\n",
              "   'lrtest': (3.1721641401891247, 0.5294386133291812, 4),\n",
              "   'params_ftest': (0.7645418744920278, 0.5494765223615958, 199.0, 4.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bef28d90>,\n",
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              " 5: ({'ssr_ftest': (0.8413821042055043, 0.5218218979622774, 196.0, 5),\n",
              "   'ssr_chi2test': (4.4430126421055975, 0.48754416586506444, 5),\n",
              "   'lrtest': (4.396002104377203, 0.4939173864743942, 5),\n",
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              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bef2ac20>,\n",
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              " 6: ({'ssr_ftest': (0.6607311528382297, 0.68145665605407, 193.0, 6),\n",
              "   'ssr_chi2test': (4.231418160145347, 0.6453913071440049, 6),\n",
              "   'lrtest': (4.188545762528975, 0.6511779750530913, 6),\n",
              "   'params_ftest': (0.6607311528382237, 0.6814566560540729, 193.0, 6.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bef2bca0>,\n",
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              "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]])]),\n",
              " 7: ({'ssr_ftest': (0.686711067264482, 0.6832263825002877, 190.0, 7),\n",
              "   'ssr_chi2test': (5.186475692234377, 0.6372183946786685, 7),\n",
              "   'lrtest': (5.1219530942958045, 0.645083532611152, 7),\n",
              "   'params_ftest': (0.6867110672644902, 0.6832263825002798, 190.0, 7.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bcc7e7d0>,\n",
              "   <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7d63bcc7d060>,\n",
              "   array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "          [0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
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              "          [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]])])}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Stock Market News\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"Stock Market News\", \"GBPUSD\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Economic Development News\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"FED News\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Micro Finance News\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"International Trade News\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_US\"]],7)\n",
        "#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_UK\"]],7)\n",
        "data['allnews'] = data['Stock Market News'] + data['Economic Development News'] + data['FED News'] +data['Micro Finance News'] +data['International Trade News']\n",
        "gc_res = grangercausalitytests(data[[\"GBPUSD\", \"allnews\"]],7)\n",
        "gc_res\n",
        "data = data.drop(\"allnews\", axis=1)\n",
        "#FED News\n",
        "#Micro Finance News\n",
        "#International Trade News\n",
        "gc_res"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cSnsSZCBiB3I"
      },
      "outputs": [],
      "source": [
        "# calculate returns of exchange rate (log FX-log of lagFX)\n",
        "#data['USDGBP'] = np.log(data['USDGBP'])-np.log(data['USDGBP'].shift(1))\n",
        "#data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5xhSYPxEzPjk"
      },
      "outputs": [],
      "source": [
        "# Take LOGS\n",
        "data['GBPUSD'] = np.log(data['GBPUSD'])\n",
        "data['CPI_US'] = np.log(data['CPI_US'])\n",
        "data['CPI_UK'] = np.log(data['CPI_UK'])\n",
        "#data['Money Market Rate_US'] = np.log(data['Money Market Rate_US'])\n",
        "#data['Money Market Rate_UK'] = np.log(data['Money Market Rate_UK'])\n",
        "data['IPI_US'] = np.log(data['IPI_US'])\n",
        "data['IPI_UK'] = np.log(data['IPI_UK'])\n",
        "data['M2_US'] = np.log(data['M2_US'])\n",
        "data['M2_UK'] = np.log(data['M2_UK'])\n",
        "data['Stock Market News'] = np.log(data['Stock Market News'])\n",
        "data['Economic Development News'] = np.log(data['Economic Development News'])\n",
        "data['FED News'] = np.log(data['FED News'])\n",
        "data['Micro Finance News'] = np.log(data['Micro Finance News'])\n",
        "data['International Trade News'] = np.log(data['International Trade News'])\n",
        "data['EPU_US'] = np.log(data['EPU_US'])\n",
        "data['EPU_UK'] = np.log(data['EPU_UK'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "dPC0tS23qXn2",
        "outputId": "710bb6e5-8aff-4589-e875-0f0e1db663a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n",
              "Date                                                             \n",
              "2000-01-01       NaN       NaN       NaN                   NaN   \n",
              "2000-02-01  0.024422  0.001826  0.003095                0.0006   \n",
              "2000-03-01  0.012355  0.001823  0.002478                0.0010   \n",
              "2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n",
              "2000-05-01  0.048292  0.000607  0.002725                0.0045   \n",
              "...              ...       ...       ...                   ...   \n",
              "2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n",
              "2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n",
              "2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n",
              "2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n",
              "2017-08-01  0.003121  0.002003  0.005507                0.0000   \n",
              "\n",
              "            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n",
              "Date                                                                       \n",
              "2000-01-01                   NaN       NaN       NaN       NaN       NaN   \n",
              "2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n",
              "2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n",
              "2000-04-01                0.0007  0.007262 -0.003527  0.011845 -0.019022   \n",
              "2000-05-01                0.0000  0.001844  0.003527 -0.002526 -0.029046   \n",
              "...                          ...       ...       ...       ...       ...   \n",
              "2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n",
              "2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n",
              "2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n",
              "2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n",
              "2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n",
              "\n",
              "            Stock Market News  Economic Development News  FED News  \\\n",
              "Date                                                                 \n",
              "2000-01-01                NaN                        NaN       NaN   \n",
              "2000-02-01           0.004850                  -0.016937 -0.003431   \n",
              "2000-03-01           0.010032                  -0.010264 -0.001292   \n",
              "2000-04-01          -0.008960                   0.019750 -0.002368   \n",
              "2000-05-01          -0.009041                   0.019368 -0.002373   \n",
              "...                       ...                        ...       ...   \n",
              "2017-04-01           0.002005                  -0.006553 -0.003000   \n",
              "2017-05-01           0.000518                  -0.022231 -0.018553   \n",
              "2017-06-01           0.001780                   0.029919  0.033112   \n",
              "2017-07-01          -0.000847                  -0.002352 -0.007995   \n",
              "2017-08-01          -0.003318                  -0.009024 -0.009132   \n",
              "\n",
              "            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n",
              "Date                                                                          \n",
              "2000-01-01                 NaN                       NaN       NaN       NaN  \n",
              "2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n",
              "2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n",
              "2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n",
              "2000-05-01            0.004203                 -0.010853  0.675874 -0.899673  \n",
              "...                        ...                       ...       ...       ...  \n",
              "2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n",
              "2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n",
              "2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n",
              "2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n",
              "2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n",
              "\n",
              "[212 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7b076d9-6163-4d79-8bf0-b7bf70d2b5b9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GBPUSD</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
              "      <th>IPI_US</th>\n",
              "      <th>IPI_UK</th>\n",
              "      <th>M2_US</th>\n",
              "      <th>M2_UK</th>\n",
              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
              "      <th>Micro Finance News</th>\n",
              "      <th>International Trade News</th>\n",
              "      <th>EPU_US</th>\n",
              "      <th>EPU_UK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>0.024422</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.003095</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.002880</td>\n",
              "      <td>0.010610</td>\n",
              "      <td>0.002831</td>\n",
              "      <td>-0.019531</td>\n",
              "      <td>0.004850</td>\n",
              "      <td>-0.016937</td>\n",
              "      <td>-0.003431</td>\n",
              "      <td>0.011022</td>\n",
              "      <td>0.000489</td>\n",
              "      <td>-0.543803</td>\n",
              "      <td>0.082941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>0.012355</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.002478</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>0.003998</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.006511</td>\n",
              "      <td>0.016098</td>\n",
              "      <td>0.010032</td>\n",
              "      <td>-0.010264</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>-0.019641</td>\n",
              "      <td>0.018035</td>\n",
              "      <td>-0.621220</td>\n",
              "      <td>0.059239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-01</th>\n",
              "      <td>-0.001989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.007262</td>\n",
              "      <td>-0.003527</td>\n",
              "      <td>0.011845</td>\n",
              "      <td>-0.019022</td>\n",
              "      <td>-0.008960</td>\n",
              "      <td>0.019750</td>\n",
              "      <td>-0.002368</td>\n",
              "      <td>0.004221</td>\n",
              "      <td>-0.010737</td>\n",
              "      <td>0.454406</td>\n",
              "      <td>0.609795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>0.048292</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.002725</td>\n",
              "      <td>0.0045</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.003527</td>\n",
              "      <td>-0.002526</td>\n",
              "      <td>-0.029046</td>\n",
              "      <td>-0.009041</td>\n",
              "      <td>0.019368</td>\n",
              "      <td>-0.002373</td>\n",
              "      <td>0.004203</td>\n",
              "      <td>-0.010853</td>\n",
              "      <td>0.675874</td>\n",
              "      <td>-0.899673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04-01</th>\n",
              "      <td>-0.023466</td>\n",
              "      <td>-0.001252</td>\n",
              "      <td>0.004547</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.010821</td>\n",
              "      <td>0.001967</td>\n",
              "      <td>0.003895</td>\n",
              "      <td>0.034527</td>\n",
              "      <td>0.002005</td>\n",
              "      <td>-0.006553</td>\n",
              "      <td>-0.003000</td>\n",
              "      <td>0.001977</td>\n",
              "      <td>0.011571</td>\n",
              "      <td>0.750910</td>\n",
              "      <td>-0.317777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>-0.022458</td>\n",
              "      <td>0.001135</td>\n",
              "      <td>0.003491</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>-0.000316</td>\n",
              "      <td>0.001963</td>\n",
              "      <td>0.004828</td>\n",
              "      <td>-0.002327</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>-0.022231</td>\n",
              "      <td>-0.018553</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>-0.022444</td>\n",
              "      <td>-0.369826</td>\n",
              "      <td>-0.059514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-01</th>\n",
              "      <td>0.008963</td>\n",
              "      <td>0.003480</td>\n",
              "      <td>-0.000242</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.001936</td>\n",
              "      <td>0.015077</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>0.029919</td>\n",
              "      <td>0.033112</td>\n",
              "      <td>-0.036240</td>\n",
              "      <td>0.010546</td>\n",
              "      <td>0.185622</td>\n",
              "      <td>0.693738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>-0.013999</td>\n",
              "      <td>0.001899</td>\n",
              "      <td>-0.000668</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.001361</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.005625</td>\n",
              "      <td>0.015743</td>\n",
              "      <td>-0.000847</td>\n",
              "      <td>-0.002352</td>\n",
              "      <td>-0.007995</td>\n",
              "      <td>0.014035</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>0.012530</td>\n",
              "      <td>-0.552083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-01</th>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.002003</td>\n",
              "      <td>0.005507</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.007291</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.003225</td>\n",
              "      <td>-0.020494</td>\n",
              "      <td>-0.003318</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>-0.009132</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>-0.113282</td>\n",
              "      <td>-0.194449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>212 rows × 16 columns</p>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "        document.querySelector('#df-e7b076d9-6163-4d79-8bf0-b7bf70d2b5b9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e7b076d9-6163-4d79-8bf0-b7bf70d2b5b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "<div id=\"df-62171421-6497-4798-bb17-a1956f220dda\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62171421-6497-4798-bb17-a1956f220dda')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62171421-6497-4798-bb17-a1956f220dda button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# First Differencing\n",
        "#data['USDGBP'] = np.log(data['USDGBP'])\n",
        "data = data.diff()\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ESPEu46m-Ewy",
        "outputId": "44913019-f2d8-4b2e-c94f-0c19ebc235f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n",
              "Date                                                             \n",
              "2000-02-01  0.024422  0.001826  0.003095                0.0006   \n",
              "2000-03-01  0.012355  0.001823  0.002478                0.0010   \n",
              "2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n",
              "2000-05-01  0.048292  0.000607  0.002725                0.0045   \n",
              "2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n",
              "...              ...       ...       ...                   ...   \n",
              "2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n",
              "2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n",
              "2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n",
              "2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n",
              "2017-08-01  0.003121  0.002003  0.005507                0.0000   \n",
              "\n",
              "            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n",
              "Date                                                                       \n",
              "2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n",
              "2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n",
              "2000-04-01                0.0007  0.007262 -0.003527  0.011845 -0.019022   \n",
              "2000-05-01                0.0000  0.001844  0.003527 -0.002526 -0.029046   \n",
              "2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n",
              "...                          ...       ...       ...       ...       ...   \n",
              "2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n",
              "2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n",
              "2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n",
              "2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n",
              "2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n",
              "\n",
              "            Stock Market News  Economic Development News  FED News  \\\n",
              "Date                                                                 \n",
              "2000-02-01           0.004850                  -0.016937 -0.003431   \n",
              "2000-03-01           0.010032                  -0.010264 -0.001292   \n",
              "2000-04-01          -0.008960                   0.019750 -0.002368   \n",
              "2000-05-01          -0.009041                   0.019368 -0.002373   \n",
              "2000-06-01          -0.002734                  -0.035576  0.013077   \n",
              "...                       ...                        ...       ...   \n",
              "2017-04-01           0.002005                  -0.006553 -0.003000   \n",
              "2017-05-01           0.000518                  -0.022231 -0.018553   \n",
              "2017-06-01           0.001780                   0.029919  0.033112   \n",
              "2017-07-01          -0.000847                  -0.002352 -0.007995   \n",
              "2017-08-01          -0.003318                  -0.009024 -0.009132   \n",
              "\n",
              "            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n",
              "Date                                                                          \n",
              "2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n",
              "2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n",
              "2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n",
              "2000-05-01            0.004203                 -0.010853  0.675874 -0.899673  \n",
              "2000-06-01            0.011326                  0.018424 -0.003626  1.055358  \n",
              "...                        ...                       ...       ...       ...  \n",
              "2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n",
              "2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n",
              "2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n",
              "2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n",
              "2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n",
              "\n",
              "[211 rows x 16 columns]"
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              "      <th>GBPUSD</th>\n",
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              "      <th>Date</th>\n",
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              "      <th></th>\n",
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              "      <th>2000-02-01</th>\n",
              "      <td>0.024422</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.003095</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.002880</td>\n",
              "      <td>0.010610</td>\n",
              "      <td>0.002831</td>\n",
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              "      <th>2000-03-01</th>\n",
              "      <td>0.012355</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.002478</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>0.003998</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.006511</td>\n",
              "      <td>0.016098</td>\n",
              "      <td>0.010032</td>\n",
              "      <td>-0.010264</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>-0.019641</td>\n",
              "      <td>0.018035</td>\n",
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              "      <th>2000-04-01</th>\n",
              "      <td>-0.001989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.007262</td>\n",
              "      <td>-0.003527</td>\n",
              "      <td>0.011845</td>\n",
              "      <td>-0.019022</td>\n",
              "      <td>-0.008960</td>\n",
              "      <td>0.019750</td>\n",
              "      <td>-0.002368</td>\n",
              "      <td>0.004221</td>\n",
              "      <td>-0.010737</td>\n",
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              "      <td>0.609795</td>\n",
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              "      <th>2000-05-01</th>\n",
              "      <td>0.048292</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.002725</td>\n",
              "      <td>0.0045</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.003527</td>\n",
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              "      <td>-0.029046</td>\n",
              "      <td>-0.009041</td>\n",
              "      <td>0.019368</td>\n",
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              "      <td>0.004203</td>\n",
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              "      <th>2000-06-01</th>\n",
              "      <td>-0.000115</td>\n",
              "      <td>0.006653</td>\n",
              "      <td>0.001442</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>0.000912</td>\n",
              "      <td>0.005268</td>\n",
              "      <td>0.003766</td>\n",
              "      <td>0.015023</td>\n",
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              "      <td>-0.035576</td>\n",
              "      <td>0.013077</td>\n",
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              "      <td>0.018424</td>\n",
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              "      <td>1.055358</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>2017-04-01</th>\n",
              "      <td>-0.023466</td>\n",
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              "      <td>0.004547</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.010821</td>\n",
              "      <td>0.001967</td>\n",
              "      <td>0.003895</td>\n",
              "      <td>0.034527</td>\n",
              "      <td>0.002005</td>\n",
              "      <td>-0.006553</td>\n",
              "      <td>-0.003000</td>\n",
              "      <td>0.001977</td>\n",
              "      <td>0.011571</td>\n",
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              "      <th>2017-05-01</th>\n",
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              "      <td>0.003491</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>-0.000316</td>\n",
              "      <td>0.001963</td>\n",
              "      <td>0.004828</td>\n",
              "      <td>-0.002327</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>-0.022231</td>\n",
              "      <td>-0.018553</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>-0.022444</td>\n",
              "      <td>-0.369826</td>\n",
              "      <td>-0.059514</td>\n",
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              "    <tr>\n",
              "      <th>2017-06-01</th>\n",
              "      <td>0.008963</td>\n",
              "      <td>0.003480</td>\n",
              "      <td>-0.000242</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.001936</td>\n",
              "      <td>0.015077</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>0.029919</td>\n",
              "      <td>0.033112</td>\n",
              "      <td>-0.036240</td>\n",
              "      <td>0.010546</td>\n",
              "      <td>0.185622</td>\n",
              "      <td>0.693738</td>\n",
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              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>-0.013999</td>\n",
              "      <td>0.001899</td>\n",
              "      <td>-0.000668</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.001361</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.005625</td>\n",
              "      <td>0.015743</td>\n",
              "      <td>-0.000847</td>\n",
              "      <td>-0.002352</td>\n",
              "      <td>-0.007995</td>\n",
              "      <td>0.014035</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>0.012530</td>\n",
              "      <td>-0.552083</td>\n",
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              "    <tr>\n",
              "      <th>2017-08-01</th>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.002003</td>\n",
              "      <td>0.005507</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.007291</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.003225</td>\n",
              "      <td>-0.020494</td>\n",
              "      <td>-0.003318</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>-0.009132</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>-0.113282</td>\n",
              "      <td>-0.194449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>211 rows × 16 columns</p>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a198a56b-10d6-46b8-a85b-c8d7ba6a7a8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#data = data.iloc[2:]\n",
        "data = data.iloc[1:]\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Jjo5-l6JZ0B",
        "outputId": "63606574-5850-4ee3-9736-b4baf69f6217"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GBPUSD                        7\n",
            "CPI_US                       12\n",
            "CPI_UK                       12\n",
            "Money Market Rate_US         41\n",
            "Money Market Rate_UK         42\n",
            "IPI_US                        7\n",
            "IPI_UK                       14\n",
            "M2_US                        13\n",
            "M2_UK                        46\n",
            "Stock Market News            18\n",
            "Economic Development News     2\n",
            "FED News                      1\n",
            "Micro Finance News            1\n",
            "International Trade News      2\n",
            "EPU_US                        1\n",
            "EPU_UK                        3\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
            "<ipython-input-15-6cc41ab41369>:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n"
          ]
        }
      ],
      "source": [
        "# Detect outliers and replace by nan\n",
        "outliers = []\n",
        "def detect_outliers_iqr(data):\n",
        "  for column in data:\n",
        "    q1 = data[column].quantile(0.25)\n",
        "    q3 = data[column].quantile(0.75)\n",
        "    #print(q1, q3)\n",
        "    IQR = q3-q1\n",
        "    lwr_bound = q1-(1.5*IQR)\n",
        "    upr_bound = q3+(1.5*IQR)\n",
        "    data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n",
        "    data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n",
        "detect_outliers_iqr(data)\n",
        "#print(data)\n",
        "print(data.isnull().sum()) # print how many outliers we have\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wl9IEuzfOmeI",
        "outputId": "3fd77399-7e3d-4898-ecbb-db28c232138c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GBPUSD                       0\n",
            "CPI_US                       0\n",
            "CPI_UK                       0\n",
            "Money Market Rate_US         0\n",
            "Money Market Rate_UK         0\n",
            "IPI_US                       0\n",
            "IPI_UK                       0\n",
            "M2_US                        0\n",
            "M2_UK                        0\n",
            "Stock Market News            0\n",
            "Economic Development News    0\n",
            "FED News                     0\n",
            "Micro Finance News           0\n",
            "International Trade News     0\n",
            "EPU_US                       0\n",
            "EPU_UK                       0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n",
            "<ipython-input-16-aa8c5292526e>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data[column].fillna(median, inplace=True)\n"
          ]
        }
      ],
      "source": [
        "# Impute Outliers\n",
        "for column in data:\n",
        "  #mean = data[column].mean()\n",
        "  #data[column].fillna(mean, inplace=True)\n",
        "  median = data[column].median()\n",
        "  data[column].fillna(median, inplace=True)\n",
        "  #print(mean)\n",
        "print(data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcVBZMf3tt0H",
        "outputId": "2ca4d4b4-3fe7-49c8-df2b-f9c1b54fd166"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GBPUSD\n",
            "3.195885974689677e-18\n",
            "CPI_US\n",
            "3.5022319410523443e-07\n",
            "CPI_UK\n",
            "0.08658238158837195\n",
            "Money Market Rate_US\n",
            "0.0070076705517349465\n",
            "Money Market Rate_UK\n",
            "2.777539860806871e-10\n",
            "IPI_US\n",
            "2.621032617088346e-07\n",
            "IPI_UK\n",
            "2.153643129942898e-30\n",
            "M2_US\n",
            "1.360520046495922e-12\n",
            "M2_UK\n",
            "7.474982369231475e-07\n",
            "Stock Market News\n",
            "8.493930772607713e-25\n",
            "Economic Development News\n",
            "0.00016433684900068142\n",
            "FED News\n",
            "0.00043924276368327776\n",
            "Micro Finance News\n",
            "0.005602860403180456\n",
            "International Trade News\n",
            "0.0006121354259344973\n",
            "EPU_US\n",
            "3.362092877603434e-17\n",
            "EPU_UK\n",
            "1.5692845900489245e-20\n"
          ]
        }
      ],
      "source": [
        "# Check if time series are normal after first differencing\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "for column in data:\n",
        "  print(column)\n",
        "  print(adfuller(data[column])[1])\n",
        "  #P value is always <0.1-> stationary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "kAlRGBOASX2x",
        "outputId": "69423a12-fb85-43c3-98ff-04959361127c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n",
              "Date                                                             \n",
              "2000-02-01  0.024422  0.001826  0.003095                0.0006   \n",
              "2000-03-01  0.012355  0.001823  0.002478                0.0010   \n",
              "2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n",
              "2000-05-01  0.048292  0.000607  0.002725                0.0000   \n",
              "2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n",
              "...              ...       ...       ...                   ...   \n",
              "2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n",
              "2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n",
              "2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n",
              "2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n",
              "2017-08-01  0.003121  0.002003  0.005507                0.0000   \n",
              "\n",
              "            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n",
              "Date                                                                       \n",
              "2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n",
              "2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n",
              "2000-04-01                0.0007  0.007262 -0.003527  0.004809 -0.019022   \n",
              "2000-05-01                0.0000  0.001844  0.003527  0.004809 -0.029046   \n",
              "2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n",
              "...                          ...       ...       ...       ...       ...   \n",
              "2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n",
              "2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n",
              "2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n",
              "2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n",
              "2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n",
              "\n",
              "            Stock Market News  Economic Development News  FED News  \\\n",
              "Date                                                                 \n",
              "2000-02-01           0.004850                  -0.016937 -0.003431   \n",
              "2000-03-01           0.010032                  -0.010264 -0.001292   \n",
              "2000-04-01          -0.008960                   0.019750 -0.002368   \n",
              "2000-05-01          -0.009041                   0.019368 -0.002373   \n",
              "2000-06-01          -0.002734                  -0.001099  0.013077   \n",
              "...                       ...                        ...       ...   \n",
              "2017-04-01           0.002005                  -0.006553 -0.003000   \n",
              "2017-05-01           0.000518                  -0.022231 -0.018553   \n",
              "2017-06-01           0.001780                  -0.001099 -0.000859   \n",
              "2017-07-01          -0.000847                  -0.002352 -0.007995   \n",
              "2017-08-01          -0.003318                  -0.009024 -0.009132   \n",
              "\n",
              "            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n",
              "Date                                                                          \n",
              "2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n",
              "2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n",
              "2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n",
              "2000-05-01            0.004203                 -0.010853  0.675874  0.020484  \n",
              "2000-06-01            0.011326                  0.018424 -0.003626  0.020484  \n",
              "...                        ...                       ...       ...       ...  \n",
              "2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n",
              "2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n",
              "2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n",
              "2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n",
              "2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n",
              "\n",
              "[211 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49579976-79f2-4052-a1a6-1af1b79b8d91\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GBPUSD</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
              "      <th>IPI_US</th>\n",
              "      <th>IPI_UK</th>\n",
              "      <th>M2_US</th>\n",
              "      <th>M2_UK</th>\n",
              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
              "      <th>Micro Finance News</th>\n",
              "      <th>International Trade News</th>\n",
              "      <th>EPU_US</th>\n",
              "      <th>EPU_UK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>0.024422</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.003095</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.002880</td>\n",
              "      <td>0.010610</td>\n",
              "      <td>0.002831</td>\n",
              "      <td>-0.019531</td>\n",
              "      <td>0.004850</td>\n",
              "      <td>-0.016937</td>\n",
              "      <td>-0.003431</td>\n",
              "      <td>0.011022</td>\n",
              "      <td>0.000489</td>\n",
              "      <td>-0.543803</td>\n",
              "      <td>0.082941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>0.012355</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.002478</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>0.003998</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.006511</td>\n",
              "      <td>0.016098</td>\n",
              "      <td>0.010032</td>\n",
              "      <td>-0.010264</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>-0.019641</td>\n",
              "      <td>0.018035</td>\n",
              "      <td>-0.621220</td>\n",
              "      <td>0.059239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-01</th>\n",
              "      <td>-0.001989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.007262</td>\n",
              "      <td>-0.003527</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>-0.019022</td>\n",
              "      <td>-0.008960</td>\n",
              "      <td>0.019750</td>\n",
              "      <td>-0.002368</td>\n",
              "      <td>0.004221</td>\n",
              "      <td>-0.010737</td>\n",
              "      <td>0.454406</td>\n",
              "      <td>0.609795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>0.048292</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.002725</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.003527</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>-0.029046</td>\n",
              "      <td>-0.009041</td>\n",
              "      <td>0.019368</td>\n",
              "      <td>-0.002373</td>\n",
              "      <td>0.004203</td>\n",
              "      <td>-0.010853</td>\n",
              "      <td>0.675874</td>\n",
              "      <td>0.020484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-06-01</th>\n",
              "      <td>-0.000115</td>\n",
              "      <td>0.006653</td>\n",
              "      <td>0.001442</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>0.000912</td>\n",
              "      <td>0.005268</td>\n",
              "      <td>0.003766</td>\n",
              "      <td>0.015023</td>\n",
              "      <td>-0.002734</td>\n",
              "      <td>-0.001099</td>\n",
              "      <td>0.013077</td>\n",
              "      <td>0.011326</td>\n",
              "      <td>0.018424</td>\n",
              "      <td>-0.003626</td>\n",
              "      <td>0.020484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04-01</th>\n",
              "      <td>-0.023466</td>\n",
              "      <td>-0.001252</td>\n",
              "      <td>0.004547</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.010821</td>\n",
              "      <td>0.001967</td>\n",
              "      <td>0.003895</td>\n",
              "      <td>0.034527</td>\n",
              "      <td>0.002005</td>\n",
              "      <td>-0.006553</td>\n",
              "      <td>-0.003000</td>\n",
              "      <td>0.001977</td>\n",
              "      <td>0.011571</td>\n",
              "      <td>0.750910</td>\n",
              "      <td>-0.317777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>-0.022458</td>\n",
              "      <td>0.001135</td>\n",
              "      <td>0.003491</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>-0.000316</td>\n",
              "      <td>0.001963</td>\n",
              "      <td>0.004828</td>\n",
              "      <td>-0.002327</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>-0.022231</td>\n",
              "      <td>-0.018553</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>-0.022444</td>\n",
              "      <td>-0.369826</td>\n",
              "      <td>-0.059514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-01</th>\n",
              "      <td>0.008963</td>\n",
              "      <td>0.003480</td>\n",
              "      <td>-0.000242</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.001936</td>\n",
              "      <td>0.015077</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>-0.001099</td>\n",
              "      <td>-0.000859</td>\n",
              "      <td>-0.036240</td>\n",
              "      <td>0.010546</td>\n",
              "      <td>0.185622</td>\n",
              "      <td>0.693738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>-0.013999</td>\n",
              "      <td>0.001899</td>\n",
              "      <td>-0.000668</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.001361</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.005625</td>\n",
              "      <td>0.015743</td>\n",
              "      <td>-0.000847</td>\n",
              "      <td>-0.002352</td>\n",
              "      <td>-0.007995</td>\n",
              "      <td>0.014035</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>0.012530</td>\n",
              "      <td>-0.552083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-01</th>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.002003</td>\n",
              "      <td>0.005507</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.007291</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.003225</td>\n",
              "      <td>-0.020494</td>\n",
              "      <td>-0.003318</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>-0.009132</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>-0.113282</td>\n",
              "      <td>-0.194449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>211 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49579976-79f2-4052-a1a6-1af1b79b8d91')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# First Differencing\n",
        "#data[\"Economic Development News\"] = data[\"Economic Development News\"].diff()\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sBJMYKsgTVrB"
      },
      "outputs": [],
      "source": [
        "#data = data.iloc[1:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "lcHSqon-mw0G",
        "outputId": "95e18901-fb95-43db-c37d-2a9061f29d2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n",
              "Date                                                             \n",
              "2000-02-01  0.024422  0.001826  0.003095                0.0006   \n",
              "2000-03-01  0.012355  0.001823  0.002478                0.0010   \n",
              "2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n",
              "2000-05-01  0.048292  0.000607  0.002725                0.0000   \n",
              "2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n",
              "...              ...       ...       ...                   ...   \n",
              "2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n",
              "2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n",
              "2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n",
              "2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n",
              "2017-08-01  0.003121  0.002003  0.005507                0.0000   \n",
              "\n",
              "            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n",
              "Date                                                                       \n",
              "2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n",
              "2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n",
              "2000-04-01                0.0007  0.007262 -0.003527  0.004809 -0.019022   \n",
              "2000-05-01                0.0000  0.001844  0.003527  0.004809 -0.029046   \n",
              "2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n",
              "...                          ...       ...       ...       ...       ...   \n",
              "2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n",
              "2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n",
              "2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n",
              "2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n",
              "2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n",
              "\n",
              "            Stock Market News  Economic Development News  FED News  \\\n",
              "Date                                                                 \n",
              "2000-02-01           0.004850                  -0.016937 -0.003431   \n",
              "2000-03-01           0.010032                  -0.010264 -0.001292   \n",
              "2000-04-01          -0.008960                   0.019750 -0.002368   \n",
              "2000-05-01          -0.009041                   0.019368 -0.002373   \n",
              "2000-06-01          -0.002734                  -0.001099  0.013077   \n",
              "...                       ...                        ...       ...   \n",
              "2017-04-01           0.002005                  -0.006553 -0.003000   \n",
              "2017-05-01           0.000518                  -0.022231 -0.018553   \n",
              "2017-06-01           0.001780                  -0.001099 -0.000859   \n",
              "2017-07-01          -0.000847                  -0.002352 -0.007995   \n",
              "2017-08-01          -0.003318                  -0.009024 -0.009132   \n",
              "\n",
              "            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n",
              "Date                                                                          \n",
              "2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n",
              "2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n",
              "2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n",
              "2000-05-01            0.004203                 -0.010853  0.675874  0.020484  \n",
              "2000-06-01            0.011326                  0.018424 -0.003626  0.020484  \n",
              "...                        ...                       ...       ...       ...  \n",
              "2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n",
              "2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n",
              "2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n",
              "2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n",
              "2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n",
              "\n",
              "[211 rows x 16 columns]"
            ],
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              "      <th></th>\n",
              "      <th>GBPUSD</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
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              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
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              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>0.024422</td>\n",
              "      <td>0.001826</td>\n",
              "      <td>0.003095</td>\n",
              "      <td>0.0006</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>0.002880</td>\n",
              "      <td>0.010610</td>\n",
              "      <td>0.002831</td>\n",
              "      <td>-0.019531</td>\n",
              "      <td>0.004850</td>\n",
              "      <td>-0.016937</td>\n",
              "      <td>-0.003431</td>\n",
              "      <td>0.011022</td>\n",
              "      <td>0.000489</td>\n",
              "      <td>-0.543803</td>\n",
              "      <td>0.082941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>0.012355</td>\n",
              "      <td>0.001823</td>\n",
              "      <td>0.002478</td>\n",
              "      <td>0.0010</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>0.003998</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.006511</td>\n",
              "      <td>0.016098</td>\n",
              "      <td>0.010032</td>\n",
              "      <td>-0.010264</td>\n",
              "      <td>-0.001292</td>\n",
              "      <td>-0.019641</td>\n",
              "      <td>0.018035</td>\n",
              "      <td>-0.621220</td>\n",
              "      <td>0.059239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-01</th>\n",
              "      <td>-0.001989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003243</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.007262</td>\n",
              "      <td>-0.003527</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>-0.019022</td>\n",
              "      <td>-0.008960</td>\n",
              "      <td>0.019750</td>\n",
              "      <td>-0.002368</td>\n",
              "      <td>0.004221</td>\n",
              "      <td>-0.010737</td>\n",
              "      <td>0.454406</td>\n",
              "      <td>0.609795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>0.048292</td>\n",
              "      <td>0.000607</td>\n",
              "      <td>0.002725</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.003527</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>-0.029046</td>\n",
              "      <td>-0.009041</td>\n",
              "      <td>0.019368</td>\n",
              "      <td>-0.002373</td>\n",
              "      <td>0.004203</td>\n",
              "      <td>-0.010853</td>\n",
              "      <td>0.675874</td>\n",
              "      <td>0.020484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-06-01</th>\n",
              "      <td>-0.000115</td>\n",
              "      <td>0.006653</td>\n",
              "      <td>0.001442</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>0.000912</td>\n",
              "      <td>0.005268</td>\n",
              "      <td>0.003766</td>\n",
              "      <td>0.015023</td>\n",
              "      <td>-0.002734</td>\n",
              "      <td>-0.001099</td>\n",
              "      <td>0.013077</td>\n",
              "      <td>0.011326</td>\n",
              "      <td>0.018424</td>\n",
              "      <td>-0.003626</td>\n",
              "      <td>0.020484</td>\n",
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              "      <th>2017-04-01</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>-0.022458</td>\n",
              "      <td>0.001135</td>\n",
              "      <td>0.003491</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>-0.000316</td>\n",
              "      <td>0.001963</td>\n",
              "      <td>0.004828</td>\n",
              "      <td>-0.002327</td>\n",
              "      <td>0.000518</td>\n",
              "      <td>-0.022231</td>\n",
              "      <td>-0.018553</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>-0.022444</td>\n",
              "      <td>-0.369826</td>\n",
              "      <td>-0.059514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-01</th>\n",
              "      <td>0.008963</td>\n",
              "      <td>0.003480</td>\n",
              "      <td>-0.000242</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.001936</td>\n",
              "      <td>0.015077</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>-0.001099</td>\n",
              "      <td>-0.000859</td>\n",
              "      <td>-0.036240</td>\n",
              "      <td>0.010546</td>\n",
              "      <td>0.185622</td>\n",
              "      <td>0.693738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>-0.013999</td>\n",
              "      <td>0.001899</td>\n",
              "      <td>-0.000668</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.001361</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.005625</td>\n",
              "      <td>0.015743</td>\n",
              "      <td>-0.000847</td>\n",
              "      <td>-0.002352</td>\n",
              "      <td>-0.007995</td>\n",
              "      <td>0.014035</td>\n",
              "      <td>-0.000650</td>\n",
              "      <td>0.012530</td>\n",
              "      <td>-0.552083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-01</th>\n",
              "      <td>0.003121</td>\n",
              "      <td>0.002003</td>\n",
              "      <td>0.005507</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0001</td>\n",
              "      <td>-0.007291</td>\n",
              "      <td>0.000972</td>\n",
              "      <td>0.003225</td>\n",
              "      <td>-0.020494</td>\n",
              "      <td>-0.003318</td>\n",
              "      <td>-0.009024</td>\n",
              "      <td>-0.009132</td>\n",
              "      <td>0.016215</td>\n",
              "      <td>-0.009463</td>\n",
              "      <td>-0.113282</td>\n",
              "      <td>-0.194449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>211 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-531384b1-e984-4ee4-914b-caaa29d80e9b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-531384b1-e984-4ee4-914b-caaa29d80e9b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-531384b1-e984-4ee4-914b-caaa29d80e9b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2557437f-c664-42d9-aba9-4bec3494a65e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2557437f-c664-42d9-aba9-4bec3494a65e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2557437f-c664-42d9-aba9-4bec3494a65e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# final data\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openpyxl"
      ],
      "metadata": {
        "id": "7g9Du_tmEVqs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_excel(\"Final_Data_GBPUSD_EPU.xlsx\")\n",
        "data.to_excel('/content/drive/MyDrive/Final_Data_GBPUSD_EPU.xlsx', index=False)"
      ],
      "metadata": {
        "id": "FZGh9Kjo7ZmW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "T62mwWOW75Gi",
        "outputId": "19aba1df-42cd-4a3b-9a53-c91b3d22d268"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           GBPUSD      CPI_US      CPI_UK  Money Market Rate_US  \\\n",
              "count  211.000000  211.000000  211.000000            211.000000   \n",
              "mean    -0.000025    0.001993    0.002132              0.000132   \n",
              "std      0.018034    0.001986    0.002754              0.000515   \n",
              "min     -0.047321   -0.003498   -0.005413             -0.001200   \n",
              "25%     -0.011570    0.000628    0.000550             -0.000100   \n",
              "50%     -0.000200    0.001868    0.002444              0.000000   \n",
              "75%      0.011091    0.003158    0.003680              0.000250   \n",
              "max      0.048292    0.007116    0.008622              0.001600   \n",
              "\n",
              "       Money Market Rate_UK      IPI_US      IPI_UK       M2_US       M2_UK  \\\n",
              "count            211.000000  211.000000  211.000000  211.000000  211.000000   \n",
              "mean               0.000029    0.001135   -0.000228    0.004763    0.004516   \n",
              "std                0.000392    0.004744    0.006531    0.002507    0.020928   \n",
              "min               -0.001100   -0.012357   -0.019083   -0.001520   -0.076999   \n",
              "25%               -0.000100   -0.002196   -0.004035    0.003272   -0.005602   \n",
              "50%                0.000000    0.001216   -0.000880    0.004809    0.004724   \n",
              "75%                0.000100    0.004179    0.003908    0.006139    0.016470   \n",
              "max                0.001100    0.012445    0.017874    0.011447    0.066326   \n",
              "\n",
              "       Stock Market News  Economic Development News    FED News  \\\n",
              "count         211.000000                 211.000000  211.000000   \n",
              "mean           -0.000324                  -0.000152   -0.000082   \n",
              "std             0.006443                   0.011207    0.011065   \n",
              "min            -0.018599                  -0.030415   -0.024708   \n",
              "25%            -0.003852                  -0.008206   -0.008092   \n",
              "50%            -0.000003                  -0.001099   -0.000859   \n",
              "75%             0.003779                   0.006298    0.007708   \n",
              "max             0.014545                   0.027623    0.024983   \n",
              "\n",
              "       Micro Finance News  International Trade News      EPU_US      EPU_UK  \n",
              "count          211.000000                211.000000  211.000000  211.000000  \n",
              "mean             0.000688                 -0.000429    0.012683    0.000352  \n",
              "std              0.025268                  0.010439    0.550068    0.289911  \n",
              "min             -0.064905                 -0.027713   -1.253588   -0.656585  \n",
              "25%             -0.016109                 -0.007946   -0.415050   -0.203735  \n",
              "50%              0.005232                 -0.000119    0.077612    0.020484  \n",
              "75%              0.015995                  0.006500    0.355336    0.209855  \n",
              "max              0.056793                  0.027952    1.502079    0.753851  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-864f8448-86b6-47e5-8f5c-399d7cd671a2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GBPUSD</th>\n",
              "      <th>CPI_US</th>\n",
              "      <th>CPI_UK</th>\n",
              "      <th>Money Market Rate_US</th>\n",
              "      <th>Money Market Rate_UK</th>\n",
              "      <th>IPI_US</th>\n",
              "      <th>IPI_UK</th>\n",
              "      <th>M2_US</th>\n",
              "      <th>M2_UK</th>\n",
              "      <th>Stock Market News</th>\n",
              "      <th>Economic Development News</th>\n",
              "      <th>FED News</th>\n",
              "      <th>Micro Finance News</th>\n",
              "      <th>International Trade News</th>\n",
              "      <th>EPU_US</th>\n",
              "      <th>EPU_UK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>211.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.000025</td>\n",
              "      <td>0.001993</td>\n",
              "      <td>0.002132</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0.000029</td>\n",
              "      <td>0.001135</td>\n",
              "      <td>-0.000228</td>\n",
              "      <td>0.004763</td>\n",
              "      <td>0.004516</td>\n",
              "      <td>-0.000324</td>\n",
              "      <td>-0.000152</td>\n",
              "      <td>-0.000082</td>\n",
              "      <td>0.000688</td>\n",
              "      <td>-0.000429</td>\n",
              "      <td>0.012683</td>\n",
              "      <td>0.000352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.018034</td>\n",
              "      <td>0.001986</td>\n",
              "      <td>0.002754</td>\n",
              "      <td>0.000515</td>\n",
              "      <td>0.000392</td>\n",
              "      <td>0.004744</td>\n",
              "      <td>0.006531</td>\n",
              "      <td>0.002507</td>\n",
              "      <td>0.020928</td>\n",
              "      <td>0.006443</td>\n",
              "      <td>0.011207</td>\n",
              "      <td>0.011065</td>\n",
              "      <td>0.025268</td>\n",
              "      <td>0.010439</td>\n",
              "      <td>0.550068</td>\n",
              "      <td>0.289911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.047321</td>\n",
              "      <td>-0.003498</td>\n",
              "      <td>-0.005413</td>\n",
              "      <td>-0.001200</td>\n",
              "      <td>-0.001100</td>\n",
              "      <td>-0.012357</td>\n",
              "      <td>-0.019083</td>\n",
              "      <td>-0.001520</td>\n",
              "      <td>-0.076999</td>\n",
              "      <td>-0.018599</td>\n",
              "      <td>-0.030415</td>\n",
              "      <td>-0.024708</td>\n",
              "      <td>-0.064905</td>\n",
              "      <td>-0.027713</td>\n",
              "      <td>-1.253588</td>\n",
              "      <td>-0.656585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.011570</td>\n",
              "      <td>0.000628</td>\n",
              "      <td>0.000550</td>\n",
              "      <td>-0.000100</td>\n",
              "      <td>-0.000100</td>\n",
              "      <td>-0.002196</td>\n",
              "      <td>-0.004035</td>\n",
              "      <td>0.003272</td>\n",
              "      <td>-0.005602</td>\n",
              "      <td>-0.003852</td>\n",
              "      <td>-0.008206</td>\n",
              "      <td>-0.008092</td>\n",
              "      <td>-0.016109</td>\n",
              "      <td>-0.007946</td>\n",
              "      <td>-0.415050</td>\n",
              "      <td>-0.203735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.000200</td>\n",
              "      <td>0.001868</td>\n",
              "      <td>0.002444</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001216</td>\n",
              "      <td>-0.000880</td>\n",
              "      <td>0.004809</td>\n",
              "      <td>0.004724</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-0.001099</td>\n",
              "      <td>-0.000859</td>\n",
              "      <td>0.005232</td>\n",
              "      <td>-0.000119</td>\n",
              "      <td>0.077612</td>\n",
              "      <td>0.020484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.011091</td>\n",
              "      <td>0.003158</td>\n",
              "      <td>0.003680</td>\n",
              "      <td>0.000250</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.004179</td>\n",
              "      <td>0.003908</td>\n",
              "      <td>0.006139</td>\n",
              "      <td>0.016470</td>\n",
              "      <td>0.003779</td>\n",
              "      <td>0.006298</td>\n",
              "      <td>0.007708</td>\n",
              "      <td>0.015995</td>\n",
              "      <td>0.006500</td>\n",
              "      <td>0.355336</td>\n",
              "      <td>0.209855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.048292</td>\n",
              "      <td>0.007116</td>\n",
              "      <td>0.008622</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>0.001100</td>\n",
              "      <td>0.012445</td>\n",
              "      <td>0.017874</td>\n",
              "      <td>0.011447</td>\n",
              "      <td>0.066326</td>\n",
              "      <td>0.014545</td>\n",
              "      <td>0.027623</td>\n",
              "      <td>0.024983</td>\n",
              "      <td>0.056793</td>\n",
              "      <td>0.027952</td>\n",
              "      <td>1.502079</td>\n",
              "      <td>0.753851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        " data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DmqXJP7m8nyk"
      },
      "outputs": [],
      "source": [
        "# Display the DataFrame in LaTeX format\n",
        "#print(data_summary.to_latex())\n",
        "\n",
        "# Export the DataFrame to a LaTeX file\n",
        "#df_summary.to_latex(\"summary.tex\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OM0aPM6QTDzq",
        "outputId": "92353f7d-074c-4359-bb7c-5084badfddc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GBPUSD\n",
            "3.195885974689677e-18\n",
            "CPI_US\n",
            "3.5022319410523443e-07\n",
            "CPI_UK\n",
            "0.08658238158837195\n",
            "Money Market Rate_US\n",
            "0.0070076705517349465\n",
            "Money Market Rate_UK\n",
            "2.777539860806871e-10\n",
            "IPI_US\n",
            "2.621032617088346e-07\n",
            "IPI_UK\n",
            "2.153643129942898e-30\n",
            "M2_US\n",
            "1.360520046495922e-12\n",
            "M2_UK\n",
            "7.474982369231475e-07\n",
            "Stock Market News\n",
            "8.493930772607713e-25\n",
            "Economic Development News\n",
            "0.00016433684900068142\n",
            "FED News\n",
            "0.00043924276368327776\n",
            "Micro Finance News\n",
            "0.005602860403180456\n",
            "International Trade News\n",
            "0.0006121354259344973\n",
            "EPU_US\n",
            "3.362092877603434e-17\n",
            "EPU_UK\n",
            "1.5692845900489245e-20\n"
          ]
        }
      ],
      "source": [
        "# Check if time series are normal after first differencing\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "for column in data:\n",
        "  print(column)\n",
        "  print(adfuller(data[column])[1])\n",
        "  #P value is always <0.1-> stationary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zRLizG4y4mqy"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "data.to_excel(\"output.xlsx\", sheet_name = 'RadyGBPUSD')\n",
        "#data.to_csv('output.csv', index = False)\n",
        "data.to_csv('GBPUSD_data.csv')\n",
        "#data\n",
        "with open('GBPUSD_data.csv', 'w') as f:\n",
        "  data.to_csv(f)\n",
        "data.to_excel('GBPUSD_data.xlsx', index=False)\n",
        "data.to_pickle(\"GBPUSD.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qj2W9X8s8HgY"
      },
      "outputs": [],
      "source": [
        "# libraries for VAR\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy\n",
        "from datetime import datetime, timedelta\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa.api import VAR\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as pdr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "pRWMWymQ8knA",
        "outputId": "d3fca698-4e0d-40a2-c6d6-fb716c390892"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.table.SimpleTable'>"
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>VAR Order Selection (* highlights the minimums)</caption>\n",
              "<tr>\n",
              "  <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>0</th> <td>    -154.4</td>  <td>    -154.2*</td> <td> 8.457e-68</td>  <td>    -154.3*</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>1</th> <td>    -155.0*</td> <td>    -150.6</td>  <td> 4.711e-68*</td> <td>    -153.2</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>2</th> <td>    -154.8</td>  <td>    -146.2</td>  <td> 6.312e-68</td>  <td>    -151.3</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>3</th> <td>    -154.1</td>  <td>    -141.4</td>  <td> 1.321e-67</td>  <td>    -149.0</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>4</th> <td>    -153.7</td>  <td>    -136.8</td>  <td> 2.452e-67</td>  <td>    -146.9</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>5</th> <td>    -153.2</td>  <td>    -132.1</td>  <td> 6.314e-67</td>  <td>    -144.6</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>6</th> <td>    -153.2</td>  <td>    -128.0</td>  <td> 1.062e-66</td>  <td>    -143.0</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>7</th> <td>    -153.7</td>  <td>    -124.3</td>  <td> 1.729e-66</td>  <td>    -141.8</td> \n",
              "</tr>\n",
              "</table>"
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lcccc}\n\\toprule\n           & \\textbf{AIC} & \\textbf{BIC} & \\textbf{FPE} & \\textbf{HQIC}  \\\\\n\\midrule\n\\textbf{0} &      -154.4  &     -154.2*  &   8.457e-68  &      -154.3*   \\\\\n\\textbf{1} &     -155.0*  &      -150.6  &  4.711e-68*  &       -153.2   \\\\\n\\textbf{2} &      -154.8  &      -146.2  &   6.312e-68  &       -151.3   \\\\\n\\textbf{3} &      -154.1  &      -141.4  &   1.321e-67  &       -149.0   \\\\\n\\textbf{4} &      -153.7  &      -136.8  &   2.452e-67  &       -146.9   \\\\\n\\textbf{5} &      -153.2  &      -132.1  &   6.314e-67  &       -144.6   \\\\\n\\textbf{6} &      -153.2  &      -128.0  &   1.062e-66  &       -143.0   \\\\\n\\textbf{7} &      -153.7  &      -124.3  &   1.729e-66  &       -141.8   \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{VAR Order Selection (* highlights the minimums)}\n\\end{center}"
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#VAR Model lag selection\n",
        "model = VAR(data) # data[:-1]\n",
        "ms = model.select_order(7)\n",
        "ms.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iDIhXJAHFTc8"
      },
      "outputs": [],
      "source": [
        "# Fit VAR with 6 lags and see the results\n",
        "results  = model.fit(7)#maxlags=10, ic='aic'\n",
        "#results.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "DdyjSKfa9kOR",
        "outputId": "f8eed39e-71f5-42ff-9ff8-fea0d1d8ca34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.table.SimpleTable'>"
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>normality (skew and kurtosis) test. H_0: data generated by normally-distributed process. Conclusion: fail to reject H_0 at 5% significance level.</caption>\n",
              "<tr>\n",
              "  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th> <th>df</th>\n",
              "</tr>\n",
              "<tr>\n",
              "       <td>20.77</td>          <td>46.19</td>      <td>0.937</td>  <td>32</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n\\midrule\n         20.77          &          46.19          &      0.937       &      32      \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{normality (skew and kurtosis) test. H_0: data generated by normally-distributed process. Conclusion: fail to reject H_0 at 5% significance level.}\n\\end{center}"
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Normality test (are residuals normally distributed?)\n",
        "nt = results.test_normality()\n",
        "nt.summary()\n",
        "# yes :) they are :)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kVpZ5RlwY4dv"
      },
      "outputs": [],
      "source": [
        "#results.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3xR0EK77S-A",
        "outputId": "0398d296-14c1-491c-eb74-532ce4d1ad06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.08830318 1.90701674 1.85604145 1.94956791 1.96168085 1.92849083\n",
            " 2.02816246 2.13655268 2.02964022 1.95640003 2.04296884 1.89256871\n",
            " 2.16783043 2.08954268 2.05131801 1.8046831 ]\n"
          ]
        }
      ],
      "source": [
        "# checking autocorrelation in residuals\n",
        "from statsmodels.stats.stattools import durbin_watson\n",
        "out = durbin_watson(results.resid)\n",
        "print(out)\n",
        "# no autocorrelation, dw statistics are always nearly 2.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "yYqAWs50cEMk",
        "outputId": "bb1b740d-c189-48c3-8511-f643767c8799"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7262199295259404e-17"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "residuals = results.resid\n",
        "residuals[\"GBPUSD\"].plot()\n",
        "residuals[\"GBPUSD\"].mean()\n",
        "#residuals[\"GBPUSD\"].var() #-8.3419732825863e-12 -1.2325951861483965e-11 9.006365659386069e-12\n",
        "#5.700218993989929e-05 5.182073259781298e-05 4.5396751666659766e-05\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8k-U3dRV3Xy",
        "outputId": "17f33ebf-b96e-459c-93b4-11d94b3c10a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.109269968215495e-05"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "residuals[\"GBPUSD\"].var()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HjQqHe6zflat"
      },
      "outputs": [],
      "source": [
        "#residuals\n",
        "#quarterly sums of USDGBP residuals are all 0!!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "nDTrc2R28lPz",
        "outputId": "fe22c433-4ac1-4589-e102-74e473c66169"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ACF\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#results.resid\n",
        "#results.resid_acorr(1)\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "plot_acf(results.resid[\"GBPUSD\"], lags=24)\n",
        "print(\"ACF\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2RW8ev6yfsui"
      },
      "outputs": [],
      "source": [
        "#results.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-JYBqjJf8ZV"
      },
      "outputs": [],
      "source": [
        "#results.plot_acorr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3w_MZGcr9Z1J",
        "outputId": "894397ac-01c1-4604-c323-221a8fc00514"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FEVD for GBPUSD\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     1.000000  0.000000  0.000000              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.753992  0.000567  0.001171              0.054820              0.000879  0.000216  0.003776  0.004638  0.161965           0.000751                   0.012459  0.001286            0.000897                  0.000021  0.000012  0.002552\n",
            "2     0.607931  0.000749  0.003516              0.070834              0.013556  0.001039  0.003289  0.071289  0.143031           0.005511                   0.017213  0.001750            0.010717                  0.022221  0.001903  0.025449\n",
            "3     0.521116  0.001663  0.003564              0.060132              0.017087  0.018716  0.002871  0.107439  0.120844           0.027954                   0.021315  0.001495            0.039258                  0.018966  0.013348  0.024232\n",
            "4     0.477648  0.009670  0.003483              0.071473              0.028372  0.024858  0.004330  0.101050  0.111351           0.024899                   0.020582  0.004300            0.035190                  0.025059  0.035130  0.022605\n",
            "5     0.431216  0.010790  0.054022              0.064719              0.036870  0.027384  0.005354  0.094044  0.100595           0.023589                   0.024039  0.010628            0.034674                  0.022651  0.034825  0.024601\n",
            "6     0.411762  0.010928  0.058500              0.069479              0.035524  0.032763  0.006883  0.091327  0.097662           0.022736                   0.029626  0.010807            0.033217                  0.021767  0.043227  0.023792\n",
            "7     0.392326  0.012515  0.060414              0.069864              0.034602  0.048552  0.006645  0.088139  0.093666           0.025182                   0.028379  0.012905            0.031679                  0.027171  0.042054  0.025908\n",
            "8     0.378336  0.013125  0.058390              0.067655              0.034253  0.048669  0.008985  0.090680  0.100075           0.027685                   0.030175  0.013767            0.032018                  0.026294  0.044589  0.025305\n",
            "9     0.365622  0.016243  0.057139              0.065297              0.034858  0.049572  0.008880  0.091569  0.096552           0.031138                   0.029111  0.014015            0.031567                  0.029001  0.048643  0.030793\n",
            "10    0.356959  0.018707  0.056001              0.064831              0.033766  0.048341  0.009868  0.089792  0.098920           0.030388                   0.028463  0.014856            0.032871                  0.038286  0.047274  0.030675\n",
            "11    0.348841  0.018699  0.055787              0.071145              0.033024  0.047213  0.010211  0.093731  0.096660           0.031463                   0.028041  0.015369            0.032755                  0.040946  0.046153  0.029962\n",
            "12    0.342194  0.019210  0.057590              0.069830              0.033378  0.046779  0.011214  0.096480  0.095454           0.030865                   0.029692  0.016183            0.032383                  0.042815  0.045496  0.030437\n",
            "13    0.334758  0.019335  0.056716              0.072353              0.034679  0.046063  0.011463  0.094620  0.096591           0.032071                   0.030048  0.015831            0.032620                  0.042393  0.048861  0.031599\n",
            "14    0.330501  0.020007  0.058323              0.071402              0.034162  0.046149  0.013556  0.093203  0.094976           0.032563                   0.030305  0.016664            0.035517                  0.042946  0.048658  0.031069\n",
            "15    0.328116  0.022840  0.057764              0.071047              0.034239  0.046619  0.014268  0.092424  0.093985           0.032327                   0.030375  0.016783            0.037389                  0.042768  0.048298  0.030759\n",
            "16    0.326245  0.022743  0.058187              0.070785              0.034165  0.046347  0.014962  0.092459  0.093497           0.033174                   0.030566  0.018035            0.037616                  0.042519  0.048015  0.030684\n",
            "17    0.324605  0.022771  0.057863              0.072343              0.034063  0.046277  0.015049  0.091931  0.093496           0.033025                   0.030383  0.018908            0.037407                  0.042279  0.048168  0.031432\n",
            "18    0.321506  0.022586  0.059153              0.072098              0.034174  0.045817  0.014956  0.091952  0.093908           0.034077                   0.031955  0.018717            0.038171                  0.042113  0.047683  0.031134\n",
            "19    0.320680  0.022729  0.059312              0.071919              0.034147  0.045798  0.015380  0.091666  0.094242           0.033971                   0.031856  0.019065            0.038203                  0.042134  0.047547  0.031352\n",
            "20    0.320406  0.022697  0.059806              0.071783              0.034146  0.046150  0.015364  0.091508  0.093954           0.033954                   0.031759  0.019023            0.038106                  0.042264  0.047459  0.031622\n",
            "21    0.319550  0.022636  0.059638              0.071788              0.034482  0.046268  0.015327  0.091256  0.093829           0.034233                   0.031731  0.018974            0.038547                  0.042797  0.047324  0.031620\n",
            "22    0.318869  0.022610  0.059550              0.071842              0.034419  0.046662  0.015428  0.091036  0.093682           0.034253                   0.031941  0.018949            0.038510                  0.043033  0.047544  0.031670\n",
            "23    0.317889  0.022806  0.059379              0.072027              0.034311  0.046701  0.015454  0.090897  0.094230           0.034146                   0.032381  0.018899            0.038634                  0.043099  0.047497  0.031648\n",
            "24    0.317084  0.022739  0.059203              0.071864              0.034231  0.046584  0.015903  0.090943  0.094075           0.034118                   0.032323  0.019483            0.039068                  0.043400  0.047358  0.031623\n",
            "25    0.316245  0.022686  0.060318              0.071695              0.034140  0.046556  0.016149  0.090796  0.093854           0.034454                   0.032282  0.019434            0.039029                  0.043319  0.047321  0.031721\n",
            "26    0.315829  0.022849  0.060418              0.071604              0.034178  0.046603  0.016162  0.090744  0.093813           0.034417                   0.032380  0.019528            0.039213                  0.043258  0.047285  0.031719\n",
            "27    0.315540  0.022849  0.060532              0.071588              0.034145  0.046571  0.016160  0.090814  0.093794           0.034413                   0.032442  0.019540            0.039453                  0.043228  0.047241  0.031690\n",
            "28    0.315192  0.022823  0.060464              0.071565              0.034226  0.046744  0.016155  0.090720  0.093794           0.034430                   0.032741  0.019520            0.039456                  0.043184  0.047234  0.031753\n",
            "29    0.314568  0.023009  0.060801              0.071782              0.034298  0.046657  0.016123  0.090683  0.093630           0.034405                   0.032821  0.019481            0.039461                  0.043101  0.047181  0.032001\n",
            "30    0.314350  0.022991  0.060759              0.071751              0.034265  0.046670  0.016202  0.090919  0.093543           0.034398                   0.032873  0.019523            0.039454                  0.043181  0.047136  0.031985\n",
            "31    0.314041  0.022968  0.060771              0.071717              0.034255  0.046624  0.016395  0.090836  0.093554           0.034478                   0.033035  0.019512            0.039459                  0.043145  0.047120  0.032089\n",
            "32    0.313751  0.023038  0.060926              0.071665              0.034225  0.046707  0.016385  0.090771  0.093629           0.034463                   0.033033  0.019574            0.039460                  0.043149  0.047136  0.032090\n",
            "33    0.313507  0.023050  0.060870              0.071673              0.034215  0.046704  0.016476  0.090695  0.093536           0.034429                   0.033445  0.019580            0.039446                  0.043211  0.047106  0.032058\n",
            "34    0.313248  0.023052  0.060929              0.071711              0.034193  0.046692  0.016626  0.090744  0.093579           0.034401                   0.033420  0.019594            0.039413                  0.043233  0.047080  0.032087\n",
            "35    0.313059  0.023036  0.060957              0.071689              0.034208  0.046748  0.016656  0.090712  0.093604           0.034476                   0.033410  0.019602            0.039390                  0.043270  0.047065  0.032118\n",
            "36    0.312898  0.023045  0.060926              0.071655              0.034191  0.046763  0.016699  0.090676  0.093558           0.034463                   0.033481  0.019625            0.039529                  0.043320  0.047067  0.032103\n",
            "37    0.312754  0.023126  0.061028              0.071678              0.034178  0.046746  0.016711  0.090631  0.093532           0.034446                   0.033497  0.019672            0.039510                  0.043300  0.047061  0.032131\n",
            "38    0.312640  0.023187  0.061043              0.071693              0.034191  0.046780  0.016705  0.090602  0.093503           0.034437                   0.033485  0.019679            0.039547                  0.043325  0.047047  0.032136\n",
            "39    0.312533  0.023195  0.061036              0.071687              0.034188  0.046769  0.016700  0.090570  0.093471           0.034430                   0.033658  0.019719            0.039573                  0.043315  0.047030  0.032127\n",
            "40    0.312373  0.023203  0.061103              0.071787              0.034201  0.046789  0.016708  0.090550  0.093446           0.034413                   0.033710  0.019707            0.039558                  0.043299  0.047017  0.032137\n",
            "41    0.312193  0.023298  0.061147              0.071773              0.034201  0.046780  0.016700  0.090514  0.093491           0.034393                   0.033727  0.019781            0.039558                  0.043303  0.046992  0.032147\n",
            "42    0.312110  0.023295  0.061141              0.071755              0.034192  0.046769  0.016708  0.090497  0.093466           0.034384                   0.033856  0.019784            0.039608                  0.043317  0.046982  0.032138\n",
            "43    0.311976  0.023290  0.061218              0.071798              0.034186  0.046765  0.016741  0.090475  0.093466           0.034371                   0.033908  0.019776            0.039607                  0.043299  0.046987  0.032138\n",
            "44    0.311861  0.023317  0.061245              0.071803              0.034174  0.046771  0.016740  0.090442  0.093530           0.034359                   0.033905  0.019809            0.039610                  0.043293  0.046969  0.032173\n",
            "45    0.311793  0.023312  0.061234              0.071788              0.034166  0.046769  0.016746  0.090431  0.093513           0.034352                   0.034003  0.019819            0.039642                  0.043304  0.046960  0.032168\n",
            "46    0.311686  0.023326  0.061246              0.071803              0.034157  0.046777  0.016795  0.090414  0.093562           0.034341                   0.034026  0.019813            0.039641                  0.043290  0.046947  0.032178\n",
            "47    0.311579  0.023355  0.061283              0.071804              0.034150  0.046797  0.016789  0.090383  0.093598           0.034330                   0.034024  0.019838            0.039653                  0.043295  0.046931  0.032189\n",
            "\n",
            "FEVD for CPI_US\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.000149  0.999851  0.000000              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.000182  0.931525  0.000167              0.002411              0.035229  0.002618  0.000006  0.005271  0.003134           0.000599                   0.000028  0.000467            0.012498                  0.005861  0.000003  0.000000\n",
            "2     0.000242  0.849563  0.000189              0.036460              0.040074  0.002918  0.007339  0.017258  0.007321           0.010400                   0.002690  0.000455            0.013840                  0.006546  0.000795  0.003909\n",
            "3     0.000346  0.815472  0.016078              0.035154              0.038706  0.004083  0.007249  0.016643  0.007092           0.014446                   0.002831  0.001236            0.013703                  0.006400  0.016622  0.003940\n",
            "4     0.002711  0.773292  0.016965              0.044934              0.046263  0.006539  0.018603  0.015621  0.006709           0.013693                   0.003796  0.001163            0.012950                  0.012259  0.019971  0.004530\n",
            "5     0.009374  0.720933  0.016945              0.045581              0.048755  0.008115  0.018875  0.014650  0.016879           0.029035                   0.003542  0.005587            0.020017                  0.011269  0.021301  0.009141\n",
            "6     0.026554  0.680369  0.019006              0.043948              0.046280  0.010549  0.018204  0.022367  0.019386           0.039596                   0.005306  0.006452            0.021661                  0.010805  0.020102  0.009415\n",
            "7     0.028343  0.633852  0.041024              0.042913              0.043536  0.010310  0.018181  0.021796  0.022193           0.066076                   0.005235  0.007384            0.020119                  0.010717  0.019575  0.008746\n",
            "8     0.029365  0.613375  0.048576              0.042878              0.042068  0.013397  0.025935  0.021031  0.023216           0.064574                   0.005922  0.007485            0.024237                  0.010405  0.018971  0.008566\n",
            "9     0.029958  0.597643  0.052680              0.049973              0.041193  0.015562  0.027265  0.020789  0.022966           0.062222                   0.005707  0.007206            0.023856                  0.010605  0.024079  0.008294\n",
            "10    0.030642  0.592861  0.054144              0.049414              0.040913  0.015716  0.027044  0.021543  0.022675           0.062422                   0.005676  0.007212            0.023889                  0.010474  0.026473  0.008903\n",
            "11    0.030899  0.577359  0.054991              0.048478              0.041932  0.026899  0.027858  0.022487  0.022646           0.061061                   0.008333  0.007530            0.024326                  0.010494  0.025926  0.008781\n",
            "12    0.030737  0.561417  0.057740              0.047518              0.042352  0.027869  0.028211  0.022165  0.022369           0.064616                   0.011655  0.007476            0.027545                  0.010214  0.026354  0.011763\n",
            "13    0.030869  0.556576  0.059676              0.047423              0.042094  0.028346  0.027967  0.022034  0.023077           0.065537                   0.011952  0.007517            0.027332                  0.011002  0.026592  0.012008\n",
            "14    0.030535  0.551465  0.059592              0.048000              0.042240  0.028131  0.027788  0.022589  0.024590           0.065281                   0.013204  0.009588            0.027035                  0.010907  0.027078  0.011975\n",
            "15    0.030418  0.546065  0.059740              0.051730              0.042135  0.028071  0.027563  0.022314  0.026334           0.064552                   0.013403  0.009691            0.026800                  0.010770  0.027951  0.012462\n",
            "16    0.030207  0.541132  0.060069              0.051488              0.043709  0.029434  0.028475  0.022383  0.026274           0.064065                   0.014462  0.009915            0.027042                  0.010981  0.027756  0.012609\n",
            "17    0.030020  0.537299  0.059741              0.051232              0.045117  0.029664  0.028494  0.023950  0.026436           0.065452                   0.014570  0.010079            0.026851                  0.010978  0.027599  0.012518\n",
            "18    0.029842  0.533892  0.059807              0.050888              0.044791  0.030357  0.028399  0.023869  0.026453           0.065464                   0.014976  0.010005            0.028282                  0.012664  0.027871  0.012440\n",
            "19    0.029645  0.531041  0.059544              0.052279              0.044636  0.030168  0.028215  0.023849  0.027247           0.065916                   0.016279  0.009953            0.028136                  0.012584  0.028085  0.012421\n",
            "20    0.029467  0.528686  0.059419              0.051965              0.044551  0.031091  0.029462  0.023715  0.027149           0.065549                   0.016726  0.010526            0.028583                  0.012595  0.027945  0.012570\n",
            "21    0.029295  0.525579  0.059200              0.052275              0.044538  0.033559  0.029508  0.023810  0.027159           0.065182                   0.017524  0.010680            0.028418                  0.012555  0.027822  0.012896\n",
            "22    0.029217  0.523325  0.058996              0.052115              0.044345  0.034788  0.029357  0.024820  0.027050           0.065515                   0.017739  0.010640            0.028949                  0.012550  0.027711  0.012883\n",
            "23    0.029297  0.522672  0.058932              0.052096              0.044422  0.034784  0.029341  0.025015  0.027122           0.065448                   0.017955  0.010708            0.028946                  0.012668  0.027702  0.012893\n",
            "24    0.029237  0.521016  0.059644              0.051926              0.044452  0.034918  0.029276  0.024960  0.027049           0.065305                   0.017943  0.010692            0.029673                  0.013231  0.027766  0.012912\n",
            "25    0.029129  0.519153  0.059586              0.054106              0.044287  0.034838  0.029178  0.025056  0.027101           0.065200                   0.018232  0.010684            0.029563                  0.013286  0.027732  0.012870\n",
            "26    0.029320  0.518121  0.059467              0.054196              0.044208  0.035216  0.029421  0.025153  0.027059           0.065197                   0.018254  0.010688            0.029607                  0.013498  0.027739  0.012853\n",
            "27    0.029426  0.517216  0.059361              0.054250              0.044361  0.035381  0.029427  0.025110  0.027053           0.065092                   0.018811  0.010683            0.029713                  0.013492  0.027788  0.012836\n",
            "28    0.029560  0.516695  0.059414              0.054310              0.044351  0.035426  0.029405  0.025159  0.027191           0.065037                   0.018819  0.010808            0.029742                  0.013480  0.027769  0.012835\n",
            "29    0.029534  0.516202  0.059585              0.054477              0.044305  0.035380  0.029368  0.025182  0.027154           0.064951                   0.018872  0.010796            0.029978                  0.013584  0.027785  0.012846\n",
            "30    0.029543  0.515815  0.059587              0.054440              0.044281  0.035423  0.029361  0.025360  0.027209           0.064901                   0.018876  0.010926            0.030032                  0.013641  0.027768  0.012838\n",
            "31    0.029512  0.515276  0.059685              0.054799              0.044231  0.035400  0.029334  0.025444  0.027194           0.064878                   0.018917  0.011078            0.030003                  0.013663  0.027749  0.012836\n",
            "32    0.029504  0.514656  0.059618              0.054832              0.044186  0.035648  0.029317  0.025544  0.027344           0.064897                   0.018901  0.011083            0.030117                  0.013684  0.027830  0.012839\n",
            "33    0.029477  0.514132  0.059634              0.054904              0.044159  0.035725  0.029290  0.025519  0.027370           0.064837                   0.019268  0.011123            0.030087                  0.013705  0.027943  0.012827\n",
            "34    0.029503  0.513887  0.059828              0.054892              0.044148  0.035708  0.029304  0.025570  0.027416           0.064798                   0.019260  0.011124            0.030094                  0.013699  0.027943  0.012825\n",
            "35    0.029491  0.513648  0.059924              0.054912              0.044155  0.035696  0.029299  0.025558  0.027407           0.064764                   0.019279  0.011120            0.030127                  0.013706  0.027928  0.012986\n",
            "36    0.029489  0.513567  0.059920              0.054904              0.044153  0.035748  0.029295  0.025556  0.027405           0.064777                   0.019276  0.011123            0.030152                  0.013724  0.027928  0.012984\n",
            "37    0.029511  0.513288  0.059903              0.054988              0.044129  0.035769  0.029311  0.025547  0.027520           0.064770                   0.019320  0.011125            0.030142                  0.013762  0.027919  0.012996\n",
            "38    0.029499  0.513056  0.059874              0.054959              0.044106  0.035941  0.029333  0.025557  0.027514           0.064785                   0.019361  0.011119            0.030202                  0.013784  0.027912  0.012998\n",
            "39    0.029493  0.512857  0.059855              0.054963              0.044126  0.035943  0.029356  0.025559  0.027541           0.064765                   0.019534  0.011115            0.030195                  0.013799  0.027903  0.012995\n",
            "40    0.029491  0.512800  0.059897              0.054964              0.044123  0.035935  0.029371  0.025565  0.027565           0.064757                   0.019529  0.011112            0.030192                  0.013797  0.027896  0.013005\n",
            "41    0.029487  0.512666  0.059897              0.054960              0.044117  0.035925  0.029368  0.025650  0.027558           0.064741                   0.019535  0.011126            0.030208                  0.013847  0.027897  0.013019\n",
            "42    0.029481  0.512573  0.059886              0.054951              0.044109  0.035949  0.029372  0.025675  0.027561           0.064729                   0.019556  0.011124            0.030252                  0.013858  0.027906  0.013017\n",
            "43    0.029477  0.512482  0.059905              0.054980              0.044101  0.035943  0.029367  0.025670  0.027573           0.064716                   0.019564  0.011126            0.030270                  0.013859  0.027938  0.013030\n",
            "44    0.029476  0.512401  0.059897              0.054999              0.044112  0.035971  0.029376  0.025667  0.027573           0.064710                   0.019582  0.011124            0.030284                  0.013863  0.027937  0.013028\n",
            "45    0.029474  0.512329  0.059889              0.054992              0.044108  0.035975  0.029381  0.025672  0.027570           0.064701                   0.019644  0.011133            0.030289                  0.013876  0.027938  0.013027\n",
            "46    0.029475  0.512255  0.059901              0.055011              0.044113  0.035991  0.029389  0.025668  0.027604           0.064695                   0.019641  0.011136            0.030287                  0.013873  0.027932  0.013028\n",
            "47    0.029473  0.512172  0.059938              0.055007              0.044113  0.035986  0.029388  0.025674  0.027603           0.064685                   0.019660  0.011155            0.030290                  0.013892  0.027935  0.013029\n",
            "\n",
            "FEVD for CPI_UK\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.011642  0.003335  0.985023              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.010420  0.031060  0.845040              0.001840              0.000104  0.036695  0.014952  0.028037  0.000292           0.003969                   0.015578  0.003763            0.001132                  0.005547  0.001387  0.000183\n",
            "2     0.009658  0.042740  0.773932              0.002466              0.000141  0.045453  0.014108  0.040491  0.000282           0.004291                   0.017818  0.007286            0.001066                  0.012508  0.002919  0.024840\n",
            "3     0.008609  0.037334  0.695228              0.018518              0.000353  0.070071  0.018058  0.035507  0.018580           0.005263                   0.020388  0.019973            0.012110                  0.011326  0.004448  0.024236\n",
            "4     0.014948  0.035918  0.661978              0.019943              0.000347  0.068034  0.021985  0.039208  0.017843           0.010175                   0.019770  0.019292            0.024849                  0.015738  0.005409  0.024562\n",
            "5     0.014503  0.035147  0.637987              0.022022              0.000366  0.076641  0.022659  0.037786  0.020076           0.011520                   0.023650  0.018718            0.032097                  0.016015  0.005595  0.025218\n",
            "6     0.013297  0.031913  0.581168              0.019881              0.024660  0.069444  0.023039  0.038364  0.027412           0.027947                   0.022739  0.017643            0.037007                  0.016315  0.020534  0.028635\n",
            "7     0.012712  0.039019  0.555299              0.019354              0.023672  0.068245  0.022597  0.045834  0.026317           0.026884                   0.028025  0.022919            0.041296                  0.016217  0.024256  0.027355\n",
            "8     0.013662  0.043190  0.536332              0.018997              0.023939  0.067278  0.021985  0.045883  0.029063           0.026426                   0.036907  0.024472            0.040024                  0.019194  0.023551  0.029096\n",
            "9     0.014039  0.042648  0.526938              0.018690              0.026339  0.066399  0.024437  0.044991  0.028553           0.026001                   0.036426  0.024296            0.043857                  0.021139  0.025212  0.030035\n",
            "10    0.016461  0.043941  0.518075              0.018470              0.026805  0.073352  0.024051  0.044203  0.028573           0.025752                   0.035792  0.023896            0.044834                  0.021304  0.024807  0.029684\n",
            "11    0.017182  0.044954  0.504390              0.018884              0.029249  0.073117  0.023913  0.042943  0.032385           0.025312                   0.036214  0.023848            0.051306                  0.021404  0.024950  0.029949\n",
            "12    0.016948  0.044853  0.500906              0.019086              0.029797  0.071312  0.023385  0.041920  0.032046           0.026708                   0.036137  0.028926            0.050711                  0.021351  0.025732  0.030182\n",
            "13    0.020283  0.048050  0.489825              0.019515              0.029251  0.070115  0.025714  0.041886  0.031975           0.026176                   0.039350  0.028682            0.052445                  0.021782  0.025254  0.029697\n",
            "14    0.020780  0.048285  0.486560              0.019921              0.029141  0.071187  0.025563  0.041859  0.033032           0.026077                   0.039449  0.028599            0.052283                  0.021760  0.025312  0.030192\n",
            "15    0.021183  0.048132  0.478912              0.024767              0.028812  0.070677  0.025260  0.041977  0.035420           0.025921                   0.042030  0.028186            0.051453                  0.021774  0.025513  0.029983\n",
            "16    0.021060  0.048533  0.475745              0.025933              0.029177  0.070925  0.026218  0.042979  0.035204           0.026084                   0.041779  0.028156            0.051101                  0.021637  0.025417  0.030054\n",
            "17    0.020934  0.048159  0.471674              0.025907              0.029708  0.072352  0.028195  0.042675  0.035273           0.026059                   0.041430  0.027976            0.052777                  0.021470  0.025525  0.029887\n",
            "18    0.020671  0.047531  0.470595              0.026321              0.030378  0.071018  0.027711  0.043450  0.034693           0.026893                   0.042868  0.027513            0.051899                  0.021063  0.025357  0.032038\n",
            "19    0.020613  0.047374  0.467842              0.026573              0.030182  0.070545  0.027622  0.043213  0.035487           0.026928                   0.043964  0.028543            0.052281                  0.021125  0.025423  0.032284\n",
            "20    0.021204  0.047318  0.465402              0.026672              0.030054  0.070608  0.027607  0.043019  0.036185           0.026738                   0.046297  0.028545            0.051907                  0.020972  0.025439  0.032032\n",
            "21    0.021229  0.048158  0.460894              0.029971              0.029794  0.070800  0.027552  0.042618  0.036824           0.026587                   0.047325  0.028754            0.051637                  0.020792  0.025278  0.031787\n",
            "22    0.021171  0.048186  0.459557              0.029895              0.029790  0.070582  0.027586  0.042549  0.036718           0.026779                   0.047932  0.028696            0.052260                  0.021152  0.025226  0.031921\n",
            "23    0.021364  0.048404  0.458085              0.029731              0.030183  0.070426  0.027650  0.042320  0.036543           0.026829                   0.047667  0.029339            0.053074                  0.021105  0.025408  0.031870\n",
            "24    0.021293  0.048237  0.457572              0.029676              0.030124  0.070164  0.027733  0.042176  0.036501           0.027490                   0.048092  0.029286            0.053023                  0.021360  0.025316  0.031958\n",
            "25    0.021150  0.048513  0.454606              0.029920              0.030006  0.070365  0.027553  0.041951  0.036575           0.027317                   0.049238  0.030007            0.054240                  0.021567  0.025249  0.031743\n",
            "26    0.021144  0.048342  0.452791              0.029877              0.030262  0.070917  0.027440  0.042071  0.037199           0.027316                   0.050433  0.029927            0.054018                  0.021499  0.025147  0.031616\n",
            "27    0.021079  0.048372  0.451395              0.030415              0.030184  0.070730  0.027399  0.042392  0.037836           0.027235                   0.050657  0.029856            0.053865                  0.021492  0.025268  0.031824\n",
            "28    0.021214  0.048371  0.450882              0.030372              0.030167  0.070656  0.027376  0.042696  0.037823           0.027212                   0.050599  0.029822            0.054016                  0.021523  0.025341  0.031929\n",
            "29    0.021224  0.048271  0.449921              0.030296              0.030291  0.070874  0.027454  0.042565  0.037842           0.027471                   0.050676  0.029859            0.054435                  0.021502  0.025320  0.031997\n",
            "30    0.021137  0.048376  0.449564              0.030374              0.030229  0.070619  0.027417  0.042387  0.038340           0.027739                   0.050721  0.029873            0.054492                  0.021436  0.025229  0.032070\n",
            "31    0.021111  0.048639  0.448567              0.030301              0.030180  0.070610  0.027384  0.042284  0.038244           0.027720                   0.051403  0.029840            0.054975                  0.021466  0.025267  0.032009\n",
            "32    0.021067  0.048558  0.447562              0.030230              0.030165  0.070819  0.027477  0.042346  0.038698           0.027653                   0.051995  0.029842            0.054866                  0.021519  0.025236  0.031965\n",
            "33    0.021068  0.048765  0.446522              0.030595              0.030128  0.070892  0.027647  0.042272  0.039255           0.027705                   0.052031  0.029774            0.054713                  0.021469  0.025174  0.031990\n",
            "34    0.021044  0.048698  0.446047              0.030579              0.030088  0.070791  0.027669  0.042510  0.039228           0.027665                   0.052166  0.029732            0.054922                  0.021628  0.025185  0.032049\n",
            "35    0.021017  0.048668  0.445500              0.030535              0.030071  0.070828  0.027858  0.042469  0.039224           0.027672                   0.052393  0.029791            0.055130                  0.021647  0.025150  0.032047\n",
            "36    0.021000  0.048731  0.445249              0.030582              0.030024  0.070744  0.027831  0.042458  0.039390           0.027656                   0.052386  0.029766            0.055194                  0.021620  0.025173  0.032198\n",
            "37    0.020966  0.048781  0.444525              0.030539              0.029979  0.070676  0.027798  0.042384  0.039401           0.027708                   0.052852  0.029892            0.055460                  0.021726  0.025163  0.032149\n",
            "38    0.020939  0.048754  0.443921              0.030563              0.029946  0.070731  0.027814  0.042377  0.039688           0.027702                   0.053185  0.029869            0.055409                  0.021830  0.025158  0.032115\n",
            "39    0.020910  0.048985  0.443344              0.030812              0.029940  0.070816  0.027789  0.042312  0.040004           0.027663                   0.053180  0.029891            0.055340                  0.021797  0.025125  0.032094\n",
            "40    0.020885  0.048940  0.442911              0.030838              0.029929  0.070758  0.027860  0.042337  0.039955           0.027635                   0.053305  0.029894            0.055553                  0.021978  0.025128  0.032094\n",
            "41    0.020882  0.048888  0.442598              0.030821              0.029958  0.070744  0.027935  0.042284  0.039942           0.027634                   0.053481  0.029864            0.055734                  0.022017  0.025110  0.032108\n",
            "42    0.020862  0.048880  0.442436              0.030891              0.029976  0.070676  0.027931  0.042233  0.040096           0.027624                   0.053515  0.029843            0.055724                  0.022001  0.025112  0.032199\n",
            "43    0.020836  0.048905  0.441943              0.030875              0.029943  0.070597  0.027899  0.042180  0.040127           0.027601                   0.053918  0.029896            0.055967                  0.022057  0.025080  0.032175\n",
            "44    0.020816  0.048882  0.441500              0.030951              0.029911  0.070631  0.027924  0.042253  0.040249           0.027573                   0.054175  0.029865            0.055941                  0.022127  0.025055  0.032146\n",
            "45    0.020791  0.049012  0.441073              0.031211              0.029913  0.070679  0.027912  0.042226  0.040495           0.027544                   0.054128  0.029860            0.055877                  0.022100  0.025027  0.032152\n",
            "46    0.020788  0.048982  0.440833              0.031201              0.029891  0.070630  0.027923  0.042242  0.040486           0.027523                   0.054193  0.029854            0.056067                  0.022190  0.025021  0.032174\n",
            "47    0.020787  0.048951  0.440505              0.031237              0.029919  0.070637  0.027977  0.042202  0.040506           0.027522                   0.054349  0.029833            0.056224                  0.022193  0.024999  0.032158\n",
            "\n",
            "FEVD for Money Market Rate_US\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.034823  0.011428  0.015148              0.938600              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.032537  0.016810  0.012385              0.835238              0.009630  0.006044  0.001767  0.024206  0.007680           0.016363                   0.014120  0.015298            0.000451                  0.003619  0.001274  0.002577\n",
            "2     0.036648  0.020902  0.010772              0.720919              0.008188  0.008105  0.018183  0.052345  0.008776           0.033385                   0.014116  0.018169            0.006731                  0.031460  0.003331  0.007970\n",
            "3     0.068645  0.018318  0.010617              0.649510              0.008921  0.035623  0.015934  0.055750  0.010649           0.029527                   0.012373  0.015818            0.012894                  0.027394  0.002943  0.025083\n",
            "4     0.065451  0.016970  0.012702              0.615943              0.010850  0.056172  0.016911  0.050697  0.009882           0.029338                   0.011634  0.014373            0.012693                  0.029632  0.007130  0.039624\n",
            "5     0.063029  0.021838  0.016073              0.580373              0.010683  0.072846  0.022203  0.049675  0.012325           0.027823                   0.014358  0.015103            0.013615                  0.032488  0.006736  0.040833\n",
            "6     0.069840  0.022581  0.015657              0.553074              0.011362  0.082975  0.021483  0.049058  0.013718           0.027005                   0.014283  0.019182            0.013054                  0.031004  0.012507  0.043217\n",
            "7     0.066808  0.024180  0.016759              0.534367              0.014743  0.089189  0.021459  0.054480  0.018440           0.025343                   0.013755  0.018002            0.012273                  0.034270  0.015375  0.040557\n",
            "8     0.064330  0.028846  0.016748              0.517672              0.017355  0.087134  0.022625  0.059229  0.019883           0.024339                   0.013472  0.018967            0.012705                  0.035796  0.021265  0.039635\n",
            "9     0.062048  0.033622  0.027209              0.497640              0.018047  0.086425  0.021808  0.057962  0.021933           0.023721                   0.013384  0.018313            0.013725                  0.035057  0.030767  0.038340\n",
            "10    0.060429  0.033325  0.033377              0.490429              0.018003  0.084013  0.022357  0.057323  0.021437           0.023266                   0.013034  0.018041            0.014824                  0.034334  0.036825  0.038984\n",
            "11    0.059381  0.034972  0.034760              0.482638              0.017614  0.084108  0.022347  0.056041  0.020961           0.022992                   0.012795  0.017722            0.015448                  0.037301  0.039870  0.041050\n",
            "12    0.059252  0.035003  0.035325              0.480205              0.017572  0.083648  0.022263  0.056916  0.020878           0.023671                   0.013399  0.017684            0.015785                  0.037310  0.040280  0.040810\n",
            "13    0.059879  0.034490  0.038270              0.473306              0.017455  0.082440  0.022437  0.056187  0.023290           0.023542                   0.016593  0.017454            0.015891                  0.037849  0.039954  0.040962\n",
            "14    0.060464  0.034194  0.039162              0.468928              0.017706  0.083849  0.023440  0.055707  0.023613           0.023387                   0.017545  0.017297            0.016924                  0.037504  0.039693  0.040587\n",
            "15    0.060776  0.034269  0.039712              0.465127              0.018841  0.083547  0.023414  0.055227  0.023413           0.023946                   0.017723  0.017473            0.017349                  0.037585  0.039482  0.042117\n",
            "16    0.060489  0.034771  0.039354              0.461649              0.019516  0.083001  0.024015  0.057478  0.023260           0.023830                   0.017828  0.018015            0.017422                  0.037302  0.039268  0.042803\n",
            "17    0.060166  0.037365  0.039646              0.459114              0.019892  0.082677  0.023959  0.057792  0.023135           0.023699                   0.017858  0.017985            0.017332                  0.037198  0.039101  0.043082\n",
            "18    0.059908  0.038065  0.039641              0.457045              0.019822  0.082484  0.023857  0.057704  0.023168           0.024530                   0.018976  0.017970            0.017294                  0.037030  0.039609  0.042897\n",
            "19    0.060355  0.039436  0.039567              0.455034              0.019969  0.082224  0.023744  0.057529  0.023086           0.025195                   0.018915  0.017904            0.017906                  0.036876  0.039567  0.042692\n",
            "20    0.060152  0.039404  0.040293              0.453550              0.019902  0.081944  0.023668  0.057334  0.023388           0.025183                   0.019720  0.017938            0.018080                  0.036815  0.039430  0.043198\n",
            "21    0.060005  0.039303  0.041135              0.452395              0.019884  0.081822  0.023641  0.057196  0.023336           0.025772                   0.019717  0.017893            0.018146                  0.036946  0.039624  0.043185\n",
            "22    0.059875  0.039317  0.041077              0.451755              0.019841  0.081683  0.023817  0.057360  0.023294           0.026297                   0.019741  0.018069            0.018205                  0.036866  0.039618  0.043186\n",
            "23    0.059980  0.039259  0.041603              0.450940              0.019802  0.081697  0.023830  0.057256  0.023546           0.026312                   0.019716  0.018031            0.018175                  0.036805  0.039954  0.043093\n",
            "24    0.059843  0.039359  0.041978              0.449829              0.020558  0.081908  0.023780  0.057192  0.023489           0.026253                   0.019681  0.017994            0.018250                  0.036716  0.040168  0.043002\n",
            "25    0.059877  0.039304  0.041882              0.448904              0.020553  0.082214  0.023810  0.057346  0.023597           0.026523                   0.019696  0.018412            0.018209                  0.036663  0.040075  0.042935\n",
            "26    0.059949  0.039301  0.041849              0.448664              0.020605  0.082288  0.023829  0.057326  0.023714           0.026509                   0.019686  0.018437            0.018195                  0.036638  0.040054  0.042956\n",
            "27    0.059951  0.039335  0.041854              0.448582              0.020635  0.082287  0.023834  0.057318  0.023716           0.026504                   0.019682  0.018436            0.018207                  0.036631  0.040078  0.042949\n",
            "28    0.059911  0.039338  0.041842              0.448291              0.020682  0.082412  0.023904  0.057302  0.023808           0.026502                   0.019672  0.018425            0.018238                  0.036606  0.040083  0.042984\n",
            "29    0.059856  0.039310  0.041804              0.448257              0.020783  0.082464  0.023930  0.057276  0.023793           0.026558                   0.019654  0.018411            0.018224                  0.036587  0.040048  0.043046\n",
            "30    0.059775  0.039267  0.041989              0.447649              0.020763  0.082686  0.023901  0.057361  0.023903           0.026522                   0.019636  0.018645            0.018205                  0.036545  0.040100  0.043052\n",
            "31    0.059748  0.039240  0.042045              0.447351              0.020770  0.082789  0.023909  0.057377  0.023903           0.026613                   0.019680  0.018635            0.018317                  0.036520  0.040081  0.043021\n",
            "32    0.059807  0.039234  0.042073              0.447200              0.020760  0.082746  0.023925  0.057400  0.023896           0.026615                   0.019723  0.018632            0.018307                  0.036514  0.040167  0.042999\n",
            "33    0.059780  0.039218  0.042094              0.447051              0.020761  0.082748  0.023924  0.057417  0.023894           0.026668                   0.019726  0.018677            0.018299                  0.036528  0.040236  0.042980\n",
            "34    0.059754  0.039203  0.042085              0.446857              0.020827  0.082829  0.023923  0.057405  0.023907           0.026672                   0.019724  0.018669            0.018444                  0.036513  0.040222  0.042965\n",
            "35    0.059738  0.039195  0.042082              0.446785              0.020825  0.082806  0.023945  0.057395  0.023955           0.026716                   0.019737  0.018682            0.018457                  0.036504  0.040224  0.042954\n",
            "36    0.059741  0.039199  0.042083              0.446701              0.020821  0.082839  0.023949  0.057400  0.023954           0.026718                   0.019749  0.018715            0.018465                  0.036499  0.040219  0.042946\n",
            "37    0.059757  0.039223  0.042081              0.446619              0.020819  0.082913  0.023949  0.057389  0.023951           0.026714                   0.019746  0.018729            0.018462                  0.036492  0.040211  0.042945\n",
            "38    0.059738  0.039219  0.042080              0.446549              0.020842  0.082886  0.023958  0.057440  0.023962           0.026770                   0.019745  0.018728            0.018458                  0.036483  0.040204  0.042936\n",
            "39    0.059731  0.039219  0.042084              0.446466              0.020847  0.082869  0.023953  0.057460  0.023992           0.026767                   0.019744  0.018788            0.018478                  0.036476  0.040196  0.042932\n",
            "40    0.059714  0.039209  0.042077              0.446351              0.020862  0.082901  0.023951  0.057477  0.024005           0.026765                   0.019777  0.018831            0.018504                  0.036466  0.040190  0.042921\n",
            "41    0.059698  0.039200  0.042108              0.446287              0.020859  0.082884  0.023955  0.057512  0.024039           0.026796                   0.019773  0.018828            0.018504                  0.036462  0.040184  0.042912\n",
            "42    0.059698  0.039197  0.042108              0.446240              0.020857  0.082895  0.023952  0.057510  0.024044           0.026798                   0.019786  0.018827            0.018510                  0.036460  0.040197  0.042921\n",
            "43    0.059697  0.039199  0.042106              0.446191              0.020858  0.082913  0.023955  0.057515  0.024052           0.026800                   0.019796  0.018829            0.018520                  0.036457  0.040196  0.042917\n",
            "44    0.059691  0.039200  0.042103              0.446161              0.020860  0.082905  0.023958  0.057514  0.024088           0.026800                   0.019794  0.018842            0.018519                  0.036454  0.040192  0.042920\n",
            "45    0.059685  0.039197  0.042117              0.446127              0.020863  0.082899  0.023962  0.057510  0.024086           0.026799                   0.019795  0.018841            0.018550                  0.036465  0.040188  0.042917\n",
            "46    0.059681  0.039194  0.042114              0.446097              0.020861  0.082911  0.023960  0.057508  0.024092           0.026813                   0.019806  0.018845            0.018554                  0.036462  0.040186  0.042915\n",
            "47    0.059678  0.039196  0.042130              0.446078              0.020860  0.082905  0.023959  0.057511  0.024096           0.026819                   0.019805  0.018844            0.018559                  0.036461  0.040183  0.042918\n",
            "\n",
            "FEVD for Money Market Rate_UK\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.000010  0.019810  0.001153              0.013877              0.965150  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.000014  0.020430  0.014060              0.012872              0.871734  0.000090  0.000130  0.004367  0.024507           0.015889                   0.013830  0.002026            0.007094                  0.000203  0.004749  0.008006\n",
            "2     0.004972  0.021299  0.016415              0.014001              0.818375  0.001931  0.000753  0.003757  0.021640           0.013876                   0.021243  0.005098            0.017543                  0.012135  0.004989  0.021973\n",
            "3     0.012776  0.037397  0.015999              0.013380              0.767499  0.015582  0.003391  0.004114  0.023020           0.022659                   0.022763  0.005863            0.016479                  0.011336  0.005196  0.022545\n",
            "4     0.011811  0.034688  0.019777              0.013253              0.735244  0.014587  0.015190  0.013623  0.029753           0.023785                   0.021240  0.005403            0.015274                  0.010682  0.015010  0.020680\n",
            "5     0.011113  0.037539  0.034093              0.014469              0.694362  0.013722  0.016980  0.012805  0.033719           0.024842                   0.026523  0.005361            0.014405                  0.010435  0.019521  0.030113\n",
            "6     0.011612  0.036393  0.033864              0.017694              0.675778  0.013426  0.017016  0.013912  0.036252           0.024776                   0.030858  0.007478            0.014088                  0.015961  0.021461  0.029429\n",
            "7     0.025412  0.035039  0.031826              0.017290              0.644786  0.012769  0.018031  0.014924  0.034890           0.032235                   0.036175  0.007651            0.016775                  0.015557  0.020562  0.036077\n",
            "8     0.028373  0.034064  0.033388              0.018645              0.630419  0.013041  0.019258  0.014836  0.038085           0.033486                   0.041117  0.007679            0.016294                  0.015560  0.020801  0.034954\n",
            "9     0.029684  0.034163  0.032845              0.021714              0.619323  0.013942  0.018916  0.014569  0.041439           0.034197                   0.040663  0.007878            0.016833                  0.016937  0.020520  0.036376\n",
            "10    0.029544  0.033982  0.033646              0.021550              0.614842  0.015036  0.019407  0.014590  0.042134           0.034307                   0.040758  0.007930            0.018256                  0.016795  0.020816  0.036406\n",
            "11    0.029189  0.033474  0.033384              0.022246              0.606830  0.017540  0.019578  0.014433  0.041640           0.036586                   0.040199  0.007812            0.018170                  0.016920  0.020599  0.041401\n",
            "12    0.031747  0.033258  0.033301              0.022116              0.602101  0.017424  0.019578  0.015378  0.041867           0.036580                   0.039884  0.007928            0.019506                  0.017157  0.020581  0.041594\n",
            "13    0.031877  0.032851  0.034317              0.025833              0.593892  0.017180  0.019556  0.015247  0.041419           0.037023                   0.042787  0.009256            0.020130                  0.017181  0.020372  0.041078\n",
            "14    0.031389  0.033142  0.036466              0.026968              0.584094  0.019085  0.019233  0.015588  0.040764           0.038458                   0.044022  0.009476            0.020441                  0.017475  0.021044  0.042356\n",
            "15    0.031212  0.032964  0.038081              0.027985              0.580842  0.020107  0.019218  0.015544  0.041035           0.038428                   0.043967  0.009480            0.020526                  0.017491  0.020968  0.042153\n",
            "16    0.031707  0.032698  0.038339              0.028207              0.576413  0.021867  0.019065  0.015834  0.041964           0.038241                   0.043823  0.010090            0.021040                  0.017772  0.020907  0.042035\n",
            "17    0.031874  0.033202  0.038167              0.028534              0.572851  0.023248  0.019359  0.017084  0.041714           0.038428                   0.043714  0.010029            0.021522                  0.017663  0.020810  0.041799\n",
            "18    0.032566  0.032833  0.038410              0.031389              0.567280  0.023398  0.019178  0.018003  0.041274           0.038847                   0.043999  0.009932            0.021697                  0.018994  0.020861  0.041339\n",
            "19    0.032694  0.032985  0.039430              0.032663              0.563169  0.023236  0.019064  0.019396  0.041248           0.038858                   0.043813  0.009980            0.021652                  0.019010  0.021639  0.041162\n",
            "20    0.032704  0.032883  0.039515              0.032846              0.561245  0.023433  0.019022  0.019329  0.041153           0.038930                   0.043659  0.009984            0.021614                  0.020484  0.021730  0.041468\n",
            "21    0.032867  0.032873  0.039442              0.033137              0.560197  0.023625  0.018987  0.019362  0.041078           0.038870                   0.043590  0.010404            0.021592                  0.020507  0.022079  0.041390\n",
            "22    0.032822  0.032812  0.039618              0.033242              0.559439  0.023679  0.018969  0.019379  0.041004           0.038839                   0.043538  0.010854            0.021625                  0.020499  0.022307  0.041373\n",
            "23    0.032800  0.032810  0.039838              0.033216              0.559062  0.023751  0.018971  0.019402  0.040972           0.038822                   0.043510  0.010856            0.021771                  0.020537  0.022328  0.041354\n",
            "24    0.032752  0.032777  0.039780              0.033617              0.558864  0.023732  0.018961  0.019376  0.040951           0.038833                   0.043448  0.010858            0.021885                  0.020551  0.022297  0.041318\n",
            "25    0.032644  0.032674  0.039792              0.033685              0.557361  0.023666  0.019250  0.019715  0.040817           0.038699                   0.043408  0.011625            0.022144                  0.020567  0.022532  0.041418\n",
            "26    0.032638  0.032638  0.039744              0.033708              0.556701  0.023669  0.019250  0.019834  0.040948           0.038684                   0.043400  0.011943            0.022118                  0.020653  0.022647  0.041423\n",
            "27    0.032623  0.032641  0.039721              0.033791              0.556400  0.023732  0.019315  0.019823  0.040928           0.038663                   0.043505  0.011949            0.022106                  0.020649  0.022752  0.041401\n",
            "28    0.032682  0.032840  0.039695              0.033792              0.556086  0.023726  0.019303  0.019815  0.040900           0.038681                   0.043499  0.011943            0.022096                  0.020655  0.022910  0.041375\n",
            "29    0.032695  0.032831  0.039673              0.033971              0.555739  0.023839  0.019300  0.019814  0.040879           0.038717                   0.043539  0.011943            0.022083                  0.020665  0.022950  0.041362\n",
            "30    0.032852  0.032821  0.039659              0.033955              0.555493  0.023840  0.019318  0.019816  0.040976           0.038707                   0.043522  0.011950            0.022111                  0.020659  0.022942  0.041379\n",
            "31    0.032845  0.032907  0.039639              0.033936              0.555210  0.023840  0.019474  0.019819  0.041033           0.038694                   0.043504  0.011943            0.022169                  0.020647  0.022939  0.041398\n",
            "32    0.032868  0.032888  0.039732              0.033978              0.554858  0.023914  0.019483  0.019812  0.041008           0.038781                   0.043477  0.012110            0.022155                  0.020637  0.022927  0.041373\n",
            "33    0.032858  0.033007  0.039762              0.033977              0.554651  0.023905  0.019477  0.019804  0.041023           0.038766                   0.043500  0.012164            0.022178                  0.020634  0.022937  0.041357\n",
            "34    0.032875  0.033018  0.039764              0.033985              0.554446  0.023980  0.019480  0.019839  0.041065           0.038777                   0.043531  0.012160            0.022170                  0.020636  0.022929  0.041346\n",
            "35    0.032864  0.033011  0.039845              0.034066              0.554196  0.023973  0.019480  0.019832  0.041076           0.038858                   0.043526  0.012180            0.022166                  0.020652  0.022933  0.041340\n",
            "36    0.032892  0.033007  0.039843              0.034068              0.554081  0.023970  0.019479  0.019836  0.041078           0.038861                   0.043523  0.012180            0.022229                  0.020672  0.022929  0.041352\n",
            "37    0.032885  0.033048  0.039835              0.034065              0.553984  0.023991  0.019490  0.019856  0.041109           0.038856                   0.043521  0.012181            0.022229                  0.020669  0.022924  0.041356\n",
            "38    0.032883  0.033041  0.039842              0.034149              0.553866  0.023989  0.019486  0.019857  0.041123           0.038880                   0.043523  0.012194            0.022226                  0.020667  0.022921  0.041354\n",
            "39    0.032877  0.033049  0.039837              0.034143              0.553775  0.024012  0.019487  0.019854  0.041122           0.038888                   0.043559  0.012199            0.022258                  0.020668  0.022926  0.041346\n",
            "40    0.032870  0.033067  0.039827              0.034139              0.553631  0.024132  0.019488  0.019855  0.041135           0.038880                   0.043585  0.012206            0.022255                  0.020663  0.022932  0.041336\n",
            "41    0.032872  0.033069  0.039901              0.034155              0.553476  0.024129  0.019496  0.019850  0.041151           0.038886                   0.043604  0.012212            0.022254                  0.020657  0.022931  0.041356\n",
            "42    0.032872  0.033065  0.039905              0.034163              0.553413  0.024134  0.019500  0.019854  0.041147           0.038884                   0.043606  0.012211            0.022285                  0.020675  0.022934  0.041352\n",
            "43    0.032871  0.033062  0.039903              0.034161              0.553353  0.024169  0.019498  0.019852  0.041146           0.038882                   0.043631  0.012213            0.022301                  0.020674  0.022931  0.041351\n",
            "44    0.032867  0.033067  0.039908              0.034165              0.553281  0.024168  0.019501  0.019856  0.041177           0.038877                   0.043625  0.012223            0.022325                  0.020671  0.022931  0.041357\n",
            "45    0.032865  0.033064  0.039907              0.034163              0.553240  0.024184  0.019503  0.019855  0.041174           0.038883                   0.043650  0.012222            0.022330                  0.020673  0.022931  0.041355\n",
            "46    0.032861  0.033066  0.039904              0.034161              0.553180  0.024228  0.019501  0.019856  0.041183           0.038886                   0.043659  0.012224            0.022331                  0.020674  0.022935  0.041351\n",
            "47    0.032857  0.033079  0.039951              0.034169              0.553098  0.024225  0.019501  0.019855  0.041192           0.038884                   0.043656  0.012226            0.022338                  0.020671  0.022934  0.041363\n",
            "\n",
            "FEVD for IPI_US\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.000054  0.018261  0.000771              0.000792              0.022564  0.957558  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.006811  0.019757  0.001620              0.002376              0.021688  0.895622  0.000196  0.003915  0.004601           0.007290                   0.020388  0.007488            0.003988                  0.000026  0.003954  0.000281\n",
            "2     0.058466  0.017199  0.003893              0.001954              0.019814  0.755285  0.003121  0.005994  0.023022           0.033675                   0.033855  0.015516            0.016187                  0.000310  0.003218  0.008490\n",
            "3     0.058671  0.017195  0.007634              0.001960              0.028751  0.715174  0.002979  0.008690  0.023404           0.031480                   0.031631  0.015039            0.015891                  0.002429  0.030418  0.008656\n",
            "4     0.065680  0.016984  0.007435              0.003418              0.034801  0.665949  0.006808  0.008152  0.023297           0.055241                   0.029442  0.014287            0.017890                  0.002280  0.028754  0.019582\n",
            "5     0.072016  0.016794  0.007254              0.003692              0.037094  0.647487  0.006736  0.007794  0.022602           0.064787                   0.028464  0.015556            0.018292                  0.005022  0.027437  0.018972\n",
            "6     0.071790  0.019605  0.007599              0.004610              0.037791  0.634086  0.006592  0.008059  0.023695           0.064022                   0.027629  0.014993            0.017895                  0.005016  0.038290  0.018327\n",
            "7     0.070859  0.020054  0.007716              0.005957              0.047998  0.619613  0.006956  0.008708  0.023446           0.062968                   0.031702  0.015179            0.017869                  0.005061  0.038003  0.017910\n",
            "8     0.081853  0.022449  0.009572              0.005970              0.047360  0.604053  0.006782  0.008859  0.022984           0.061479                   0.032056  0.015639            0.019246                  0.005033  0.037479  0.019185\n",
            "9     0.079812  0.022210  0.010073              0.006201              0.055165  0.590575  0.006613  0.011967  0.022485           0.062453                   0.033702  0.015577            0.020345                  0.006021  0.038043  0.018758\n",
            "10    0.078770  0.022576  0.010015              0.006607              0.054860  0.582065  0.006887  0.011802  0.023652           0.065275                   0.034764  0.017726            0.021416                  0.005942  0.038984  0.018660\n",
            "11    0.078787  0.022887  0.010057              0.006792              0.054589  0.575621  0.008159  0.013015  0.023397           0.066560                   0.034666  0.018401            0.021634                  0.008379  0.038566  0.018491\n",
            "12    0.078582  0.023973  0.010132              0.010396              0.054258  0.568716  0.008252  0.012865  0.024017           0.066512                   0.035506  0.019281            0.021430                  0.009613  0.038127  0.018340\n",
            "13    0.078724  0.024226  0.010107              0.010373              0.054189  0.566843  0.008229  0.012819  0.024223           0.066279                   0.036903  0.019453            0.021456                  0.009635  0.038262  0.018278\n",
            "14    0.077937  0.024491  0.010122              0.010282              0.053593  0.562016  0.008131  0.012839  0.024251           0.066804                   0.036803  0.019335            0.021254                  0.009703  0.042343  0.020098\n",
            "15    0.077858  0.024369  0.010101              0.010454              0.053878  0.559744  0.008160  0.013014  0.024244           0.066759                   0.037376  0.019277            0.021148                  0.011047  0.042419  0.020153\n",
            "16    0.077870  0.024466  0.010269              0.012122              0.053611  0.556920  0.008398  0.012947  0.024336           0.066691                   0.037213  0.019780            0.021581                  0.011508  0.042231  0.020058\n",
            "17    0.077638  0.024460  0.010232              0.012078              0.053440  0.555278  0.008493  0.012951  0.024363           0.067787                   0.037136  0.020201            0.021781                  0.012066  0.042084  0.020013\n",
            "18    0.077436  0.024564  0.010213              0.012066              0.053308  0.553853  0.008708  0.012986  0.024380           0.067897                   0.037053  0.020264            0.021758                  0.013134  0.042144  0.020237\n",
            "19    0.077869  0.024504  0.010811              0.012415              0.053241  0.552248  0.008682  0.013368  0.024442           0.067883                   0.037010  0.020206            0.021714                  0.013324  0.042034  0.020247\n",
            "20    0.077966  0.024572  0.010960              0.012410              0.053204  0.551436  0.008667  0.013466  0.024491           0.067964                   0.036954  0.020199            0.021855                  0.013396  0.042082  0.020378\n",
            "21    0.077907  0.024689  0.010990              0.012401              0.053162  0.551041  0.008661  0.013661  0.024552           0.067986                   0.037006  0.020194            0.021840                  0.013392  0.042050  0.020469\n",
            "22    0.077862  0.024710  0.010982              0.012811              0.053103  0.550457  0.008662  0.013646  0.024638           0.067909                   0.037034  0.020177            0.021821                  0.013379  0.042266  0.020542\n",
            "23    0.077807  0.024743  0.011072              0.012814              0.053053  0.550015  0.008713  0.013656  0.024784           0.068006                   0.037002  0.020250            0.021845                  0.013456  0.042227  0.020558\n",
            "24    0.077745  0.024886  0.011200              0.012834              0.053031  0.549659  0.008716  0.013800  0.024771           0.067963                   0.037013  0.020253            0.021901                  0.013491  0.042192  0.020545\n",
            "25    0.077769  0.024880  0.011213              0.013119              0.053034  0.549342  0.008718  0.013792  0.024768           0.067959                   0.037004  0.020245            0.021932                  0.013483  0.042207  0.020533\n",
            "26    0.077725  0.024881  0.011207              0.013126              0.053011  0.549135  0.008738  0.013892  0.024793           0.067948                   0.036982  0.020265            0.021934                  0.013634  0.042208  0.020522\n",
            "27    0.077686  0.024979  0.011313              0.013165              0.052983  0.548856  0.008772  0.013886  0.024810           0.067918                   0.037001  0.020353            0.021946                  0.013630  0.042190  0.020513\n",
            "28    0.077643  0.024966  0.011326              0.013158              0.052956  0.548595  0.009031  0.013882  0.024806           0.067897                   0.036990  0.020345            0.021935                  0.013624  0.042279  0.020567\n",
            "29    0.077616  0.024971  0.011355              0.013154              0.052937  0.548543  0.009071  0.013879  0.024827           0.067872                   0.036979  0.020389            0.021931                  0.013621  0.042263  0.020593\n",
            "30    0.077598  0.024979  0.011353              0.013155              0.052976  0.548415  0.009070  0.013899  0.024875           0.067858                   0.036998  0.020424            0.021929                  0.013628  0.042254  0.020589\n",
            "31    0.077568  0.025028  0.011363              0.013171              0.052982  0.548180  0.009171  0.013932  0.024869           0.067848                   0.037023  0.020417            0.021926                  0.013624  0.042262  0.020634\n",
            "32    0.077560  0.025025  0.011368              0.013172              0.052981  0.548125  0.009172  0.013963  0.024873           0.067861                   0.037019  0.020444            0.021923                  0.013625  0.042259  0.020631\n",
            "33    0.077554  0.025019  0.011367              0.013183              0.052972  0.548008  0.009172  0.014010  0.024868           0.067893                   0.037054  0.020443            0.021926                  0.013634  0.042265  0.020631\n",
            "34    0.077550  0.025109  0.011391              0.013181              0.052966  0.547909  0.009199  0.014009  0.024864           0.067881                   0.037047  0.020447            0.021922                  0.013631  0.042266  0.020630\n",
            "35    0.077533  0.025122  0.011418              0.013212              0.052963  0.547778  0.009198  0.014012  0.024865           0.067870                   0.037046  0.020493            0.021923                  0.013631  0.042265  0.020672\n",
            "36    0.077525  0.025136  0.011417              0.013210              0.052973  0.547713  0.009216  0.014010  0.024863           0.067863                   0.037054  0.020530            0.021924                  0.013636  0.042260  0.020670\n",
            "37    0.077515  0.025131  0.011420              0.013220              0.052973  0.547608  0.009257  0.014012  0.024875           0.067853                   0.037075  0.020529            0.021941                  0.013633  0.042268  0.020691\n",
            "38    0.077506  0.025129  0.011435              0.013235              0.052966  0.547542  0.009258  0.014017  0.024891           0.067845                   0.037071  0.020577            0.021939                  0.013633  0.042263  0.020694\n",
            "39    0.077502  0.025128  0.011437              0.013235              0.052966  0.547513  0.009259  0.014018  0.024894           0.067843                   0.037101  0.020577            0.021940                  0.013632  0.042261  0.020694\n",
            "40    0.077496  0.025135  0.011436              0.013235              0.052965  0.547481  0.009271  0.014026  0.024893           0.067839                   0.037106  0.020575            0.021956                  0.013632  0.042259  0.020696\n",
            "41    0.077489  0.025132  0.011449              0.013257              0.052960  0.547432  0.009273  0.014025  0.024900           0.067835                   0.037105  0.020591            0.021954                  0.013631  0.042256  0.020710\n",
            "42    0.077493  0.025132  0.011451              0.013257              0.052958  0.547413  0.009279  0.014024  0.024900           0.067832                   0.037105  0.020596            0.021961                  0.013632  0.042257  0.020710\n",
            "43    0.077488  0.025138  0.011452              0.013261              0.052963  0.547387  0.009283  0.014026  0.024900           0.067830                   0.037111  0.020594            0.021970                  0.013631  0.042255  0.020712\n",
            "44    0.077483  0.025141  0.011457              0.013271              0.052960  0.547364  0.009283  0.014025  0.024910           0.067828                   0.037108  0.020604            0.021970                  0.013630  0.042253  0.020712\n",
            "45    0.077478  0.025143  0.011457              0.013270              0.052957  0.547331  0.009284  0.014028  0.024916           0.067824                   0.037136  0.020607            0.021974                  0.013630  0.042253  0.020712\n",
            "46    0.077473  0.025145  0.011461              0.013270              0.052954  0.547304  0.009290  0.014033  0.024918           0.067819                   0.037140  0.020612            0.021986                  0.013629  0.042251  0.020714\n",
            "47    0.077468  0.025145  0.011481              0.013274              0.052951  0.547268  0.009299  0.014032  0.024926           0.067817                   0.037139  0.020619            0.021984                  0.013628  0.042250  0.020718\n",
            "\n",
            "FEVD for IPI_UK\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.001985  0.004979  0.002911              0.024200              0.000263  0.001969  0.963694  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.008361  0.016947  0.011947              0.017639              0.006148  0.081699  0.776965  0.003144  0.001619           0.000265                   0.007242  0.047518            0.002485                  0.005224  0.003975  0.008824\n",
            "2     0.010205  0.029768  0.011142              0.016650              0.023806  0.079728  0.728856  0.003248  0.004654           0.003623                   0.007259  0.051028            0.002593                  0.005316  0.011712  0.010412\n",
            "3     0.025649  0.041190  0.010310              0.031963              0.022439  0.071075  0.646622  0.009581  0.007799           0.026353                   0.007658  0.053236            0.002911                  0.019625  0.010705  0.012886\n",
            "4     0.024894  0.042899  0.015487              0.029941              0.021296  0.066613  0.606383  0.010740  0.008201           0.025026                   0.010126  0.078791            0.003217                  0.018691  0.012939  0.024759\n",
            "5     0.031438  0.040525  0.015168              0.029763              0.021298  0.078510  0.567714  0.010832  0.009477           0.050587                   0.010245  0.073567            0.003579                  0.017423  0.012585  0.027288\n",
            "6     0.030665  0.066640  0.013961              0.027065              0.020050  0.071391  0.514223  0.009949  0.013717           0.075878                   0.009316  0.069134            0.017314                  0.019199  0.011409  0.030091\n",
            "7     0.031524  0.068344  0.013916              0.027278              0.019690  0.070615  0.501019  0.010840  0.021374           0.073772                   0.009524  0.067914            0.018990                  0.019831  0.011185  0.034182\n",
            "8     0.032753  0.066700  0.013892              0.027744              0.018924  0.068036  0.481489  0.011941  0.020488           0.077643                   0.011879  0.081810            0.020578                  0.019249  0.012738  0.034135\n",
            "9     0.035057  0.070828  0.013465              0.026923              0.018504  0.065943  0.466793  0.017598  0.019858           0.077036                   0.022105  0.080411            0.020042                  0.019362  0.012459  0.033616\n",
            "10    0.033712  0.070835  0.026364              0.029914              0.018002  0.065897  0.447416  0.020177  0.018970           0.073657                   0.026243  0.078332            0.019219                  0.018602  0.017169  0.035489\n",
            "11    0.033478  0.071708  0.026370              0.030186              0.018221  0.068420  0.442932  0.020426  0.019141           0.073189                   0.027585  0.077776            0.019160                  0.019134  0.017130  0.035145\n",
            "12    0.032234  0.069184  0.033481              0.032271              0.017925  0.065887  0.426731  0.030741  0.019488           0.071679                   0.029084  0.075551            0.018449                  0.018462  0.019145  0.039687\n",
            "13    0.032659  0.068351  0.033131              0.031894              0.019557  0.065095  0.423576  0.031168  0.019574           0.071350                   0.029628  0.074668            0.018472                  0.020282  0.019208  0.041387\n",
            "14    0.033272  0.069810  0.032950              0.031684              0.019424  0.065503  0.420617  0.031240  0.019755           0.070881                   0.029455  0.074424            0.020360                  0.020157  0.019190  0.041276\n",
            "15    0.032947  0.069809  0.036364              0.031521              0.019098  0.071956  0.413657  0.030894  0.019420           0.070709                   0.029634  0.073204            0.020202                  0.021035  0.018953  0.040598\n",
            "16    0.032905  0.070449  0.036438              0.031667              0.019420  0.071352  0.410038  0.030616  0.019461           0.070895                   0.032000  0.072548            0.020129                  0.022399  0.018920  0.040764\n",
            "17    0.032759  0.070105  0.036956              0.031572              0.019709  0.071168  0.408034  0.031347  0.019379           0.071075                   0.031852  0.072518            0.020595                  0.022770  0.019338  0.040823\n",
            "18    0.032649  0.070116  0.037935              0.031778              0.019804  0.070831  0.406150  0.031280  0.019461           0.070761                   0.031898  0.072189            0.020507                  0.022687  0.020016  0.041938\n",
            "19    0.032532  0.069791  0.037831              0.031611              0.019708  0.070619  0.407314  0.031583  0.019681           0.070706                   0.031735  0.071822            0.020399                  0.022742  0.019949  0.041979\n",
            "20    0.032666  0.069539  0.037665              0.031476              0.019666  0.070712  0.405898  0.031567  0.019588           0.070518                   0.031855  0.071677            0.022103                  0.023232  0.020034  0.041803\n",
            "21    0.032926  0.069986  0.038202              0.031499              0.019699  0.070704  0.404500  0.031790  0.019526           0.070275                   0.031748  0.071713            0.022372                  0.023158  0.019966  0.041938\n",
            "22    0.032890  0.070459  0.038151              0.031439              0.019678  0.070655  0.403874  0.031764  0.019500           0.070153                   0.031901  0.071811            0.022329                  0.023201  0.020014  0.042182\n",
            "23    0.032879  0.070376  0.038393              0.031481              0.019696  0.070554  0.403268  0.031741  0.019477           0.070084                   0.032120  0.072182            0.022411                  0.023170  0.019997  0.042171\n",
            "24    0.032840  0.070429  0.038639              0.031622              0.019656  0.070440  0.402453  0.032555  0.019510           0.069929                   0.032099  0.072046            0.022385                  0.023274  0.019968  0.042154\n",
            "25    0.032917  0.070454  0.038705              0.031918              0.019727  0.070592  0.401758  0.032568  0.019477           0.069966                   0.032285  0.071974            0.022347                  0.023236  0.019944  0.042131\n",
            "26    0.032899  0.070320  0.039034              0.032027              0.019745  0.070411  0.400770  0.032657  0.019466           0.070185                   0.032248  0.072513            0.022291                  0.023193  0.020195  0.042049\n",
            "27    0.032966  0.070205  0.038981              0.032159              0.019728  0.070485  0.400275  0.032724  0.019503           0.070167                   0.032280  0.072486            0.022250                  0.023150  0.020239  0.042402\n",
            "28    0.032940  0.070162  0.039074              0.032132              0.019937  0.070579  0.399939  0.032746  0.019490           0.070143                   0.032257  0.072537            0.022251                  0.023181  0.020262  0.042371\n",
            "29    0.032951  0.070160  0.039062              0.032163              0.019931  0.070677  0.399561  0.032737  0.019484           0.070077                   0.032294  0.072606            0.022351                  0.023234  0.020323  0.042389\n",
            "30    0.032919  0.070498  0.039095              0.032165              0.020095  0.070781  0.399154  0.032718  0.019482           0.070039                   0.032261  0.072588            0.022341                  0.023210  0.020307  0.042345\n",
            "31    0.032905  0.070480  0.039049              0.032575              0.020126  0.070702  0.398690  0.032814  0.019466           0.070014                   0.032294  0.072680            0.022320                  0.023268  0.020292  0.042327\n",
            "32    0.032941  0.070448  0.039058              0.032691              0.020171  0.070674  0.398472  0.032842  0.019477           0.069976                   0.032332  0.072673            0.022395                  0.023258  0.020288  0.042304\n",
            "33    0.032953  0.070467  0.039103              0.032732              0.020310  0.070753  0.398222  0.032844  0.019540           0.069937                   0.032303  0.072630            0.022376                  0.023238  0.020271  0.042321\n",
            "34    0.032939  0.070471  0.039350              0.032782              0.020394  0.070703  0.397712  0.032812  0.019618           0.069954                   0.032332  0.072768            0.022348                  0.023247  0.020246  0.042323\n",
            "35    0.032924  0.070430  0.039345              0.032875              0.020434  0.070694  0.397400  0.032810  0.019603           0.069939                   0.032420  0.072748            0.022506                  0.023295  0.020287  0.042290\n",
            "36    0.032929  0.070392  0.039323              0.032954              0.020440  0.070813  0.397174  0.032834  0.019681           0.069902                   0.032432  0.072745            0.022510                  0.023306  0.020287  0.042278\n",
            "37    0.032918  0.070358  0.039314              0.032972              0.020458  0.070792  0.396973  0.032835  0.019796           0.069872                   0.032476  0.072776            0.022588                  0.023308  0.020281  0.042282\n",
            "38    0.032917  0.070357  0.039308              0.032964              0.020482  0.070783  0.396875  0.032830  0.019796           0.069861                   0.032528  0.072767            0.022671                  0.023305  0.020282  0.042275\n",
            "39    0.032905  0.070381  0.039311              0.033027              0.020489  0.070780  0.396699  0.032864  0.019842           0.069916                   0.032519  0.072769            0.022663                  0.023295  0.020273  0.042268\n",
            "40    0.032900  0.070404  0.039345              0.033011              0.020478  0.070755  0.396497  0.032851  0.019889           0.069924                   0.032505  0.072806            0.022768                  0.023301  0.020270  0.042295\n",
            "41    0.032889  0.070384  0.039339              0.033018              0.020501  0.070747  0.396374  0.032842  0.019891           0.069901                   0.032592  0.072787            0.022878                  0.023305  0.020270  0.042282\n",
            "42    0.032878  0.070359  0.039368              0.033040              0.020497  0.070763  0.396234  0.032835  0.019984           0.069922                   0.032637  0.072784            0.022871                  0.023299  0.020262  0.042269\n",
            "43    0.032865  0.070358  0.039465              0.033026              0.020490  0.070743  0.396078  0.032821  0.020050           0.069897                   0.032685  0.072754            0.022955                  0.023289  0.020253  0.042270\n",
            "44    0.032860  0.070345  0.039455              0.033017              0.020486  0.070749  0.395977  0.032827  0.020048           0.069885                   0.032808  0.072752            0.022996                  0.023283  0.020249  0.042263\n",
            "45    0.032847  0.070360  0.039520              0.033020              0.020478  0.070730  0.395853  0.032855  0.020116           0.069873                   0.032799  0.072755            0.022987                  0.023291  0.020242  0.042273\n",
            "46    0.032854  0.070361  0.039573              0.033011              0.020475  0.070715  0.395745  0.032847  0.020151           0.069853                   0.032798  0.072738            0.023058                  0.023288  0.020236  0.042297\n",
            "47    0.032847  0.070346  0.039576              0.033007              0.020471  0.070715  0.395680  0.032841  0.020176           0.069853                   0.032843  0.072723            0.023098                  0.023297  0.020236  0.042291\n",
            "\n",
            "FEVD for M2_US\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.000170  0.013587  0.014622              0.002785              0.022567  0.002905  0.004985  0.938381  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.007700  0.011836  0.015087              0.012937              0.037593  0.028731  0.007875  0.807425  0.000545           0.004526                   0.025841  0.019242            0.011409                  0.000764  0.001884  0.006605\n",
            "2     0.009551  0.016086  0.017842              0.024699              0.060707  0.042041  0.022567  0.634091  0.001791           0.038424                   0.019881  0.067273            0.008778                  0.001199  0.028565  0.006505\n",
            "3     0.024831  0.015931  0.022696              0.023601              0.065607  0.040861  0.026189  0.588862  0.009796           0.035681                   0.028838  0.065841            0.009977                  0.003891  0.030500  0.006900\n",
            "4     0.025493  0.014975  0.023548              0.022960              0.064948  0.038719  0.031689  0.555841  0.012228           0.041588                   0.032012  0.065653            0.019989                  0.003726  0.033880  0.012751\n",
            "5     0.027733  0.015174  0.025922              0.022936              0.064303  0.036912  0.030091  0.529299  0.015190           0.040594                   0.031409  0.062100            0.023755                  0.003532  0.035809  0.035238\n",
            "6     0.026388  0.015112  0.026613              0.021970              0.062354  0.040991  0.034387  0.505938  0.017853           0.038911                   0.041393  0.060855            0.025374                  0.003469  0.044560  0.033831\n",
            "7     0.029917  0.015976  0.025926              0.021324              0.061609  0.038917  0.048065  0.480847  0.017065           0.041180                   0.046956  0.064500            0.024583                  0.003973  0.045357  0.033805\n",
            "8     0.029595  0.016089  0.026175              0.025562              0.060811  0.039129  0.050244  0.472177  0.017138           0.040549                   0.048050  0.064192            0.027169                  0.005354  0.044534  0.033234\n",
            "9     0.029817  0.016053  0.025812              0.025363              0.060414  0.038817  0.052390  0.468383  0.017027           0.041344                   0.048332  0.065186            0.027210                  0.006320  0.043949  0.033583\n",
            "10    0.030473  0.018315  0.026393              0.026126              0.059747  0.038924  0.053630  0.463038  0.016879           0.041813                   0.048045  0.064721            0.028986                  0.006240  0.043453  0.033219\n",
            "11    0.030561  0.019685  0.026394              0.025936              0.059446  0.039600  0.053372  0.462386  0.017136           0.041954                   0.047693  0.064368            0.028920                  0.006201  0.043353  0.032996\n",
            "12    0.030114  0.020361  0.026460              0.026293              0.060566  0.039547  0.052555  0.454544  0.020500           0.041387                   0.046897  0.063471            0.034052                  0.007867  0.042638  0.032748\n",
            "13    0.029843  0.023896  0.026233              0.026083              0.061395  0.039274  0.052124  0.450488  0.020489           0.041195                   0.046864  0.064048            0.033922                  0.009146  0.042259  0.032740\n",
            "14    0.029684  0.024578  0.026069              0.025912              0.060858  0.041112  0.052257  0.447327  0.020355           0.041152                   0.046836  0.065456            0.033666                  0.010075  0.041965  0.032699\n",
            "15    0.029458  0.024635  0.027228              0.025802              0.061592  0.041366  0.051860  0.445137  0.020278           0.041719                   0.047912  0.064959            0.033517                  0.010227  0.041647  0.032661\n",
            "16    0.029281  0.024502  0.027100              0.026195              0.061342  0.044044  0.051591  0.442543  0.020382           0.041469                   0.047642  0.064569            0.034611                  0.010514  0.041733  0.032484\n",
            "17    0.029546  0.024437  0.027347              0.027003              0.061211  0.045178  0.051407  0.440991  0.020409           0.041340                   0.047568  0.064350            0.034595                  0.010490  0.041669  0.032459\n",
            "18    0.029427  0.024430  0.027255              0.027058              0.060982  0.045243  0.051214  0.439929  0.020336           0.041183                   0.047640  0.066343            0.034605                  0.010448  0.041502  0.032404\n",
            "19    0.029449  0.024460  0.027189              0.027148              0.060794  0.045089  0.051049  0.438529  0.020362           0.042081                   0.047699  0.066156            0.035114                  0.010680  0.041393  0.032807\n",
            "20    0.029355  0.024673  0.027288              0.027529              0.061311  0.044924  0.050989  0.436902  0.020783           0.041948                   0.047616  0.066202            0.035183                  0.011206  0.041400  0.032691\n",
            "21    0.029534  0.024643  0.027380              0.027561              0.061205  0.044998  0.050814  0.436992  0.020712           0.041807                   0.047452  0.066028            0.035335                  0.011690  0.041263  0.032587\n",
            "22    0.029515  0.024601  0.028025              0.027520              0.061085  0.044899  0.050946  0.436277  0.020949           0.041807                   0.047460  0.065893            0.035362                  0.011836  0.041282  0.032544\n",
            "23    0.029538  0.024778  0.028034              0.027603              0.061120  0.044938  0.050921  0.435892  0.020931           0.041768                   0.047580  0.065868            0.035380                  0.011828  0.041304  0.032518\n",
            "24    0.029547  0.024763  0.028018              0.027646              0.061146  0.044973  0.051025  0.435658  0.020926           0.041848                   0.047551  0.065835            0.035385                  0.011887  0.041288  0.032506\n",
            "25    0.029563  0.024761  0.028026              0.027654              0.061095  0.045073  0.050988  0.435401  0.020913           0.042024                   0.047657  0.065783            0.035414                  0.011880  0.041287  0.032480\n",
            "26    0.029547  0.024793  0.028017              0.027688              0.061075  0.045073  0.051033  0.435179  0.020915           0.042078                   0.047686  0.065759            0.035454                  0.011920  0.041317  0.032465\n",
            "27    0.029511  0.024806  0.028061              0.027653              0.061016  0.045011  0.051102  0.434580  0.021025           0.042019                   0.047642  0.065694            0.035584                  0.012514  0.041361  0.032420\n",
            "28    0.029559  0.024794  0.028214              0.027842              0.060975  0.044993  0.051082  0.434351  0.021043           0.041992                   0.047626  0.065653            0.035573                  0.012518  0.041337  0.032447\n",
            "29    0.029542  0.024848  0.028208              0.027863              0.060936  0.044978  0.051126  0.434113  0.021043           0.041963                   0.047789  0.065662            0.035570                  0.012541  0.041313  0.032505\n",
            "30    0.029538  0.024852  0.028199              0.027880              0.060933  0.045023  0.051114  0.433962  0.021130           0.041953                   0.047854  0.065639            0.035590                  0.012542  0.041298  0.032494\n",
            "31    0.029566  0.024841  0.028227              0.027875              0.060907  0.045060  0.051095  0.433854  0.021123           0.041940                   0.047929  0.065612            0.035660                  0.012548  0.041282  0.032480\n",
            "32    0.029578  0.024831  0.028319              0.027875              0.060886  0.045054  0.051107  0.433729  0.021128           0.041985                   0.047911  0.065595            0.035669                  0.012560  0.041292  0.032479\n",
            "33    0.029583  0.024820  0.028444              0.027933              0.060857  0.045044  0.051121  0.433529  0.021127           0.041986                   0.047905  0.065568            0.035657                  0.012563  0.041312  0.032552\n",
            "34    0.029571  0.024813  0.028455              0.028230              0.060835  0.045029  0.051106  0.433376  0.021119           0.041969                   0.047891  0.065543            0.035648                  0.012565  0.041296  0.032552\n",
            "35    0.029579  0.024810  0.028449              0.028228              0.060848  0.045020  0.051098  0.433366  0.021155           0.041960                   0.047912  0.065528            0.035641                  0.012570  0.041292  0.032546\n",
            "36    0.029578  0.024829  0.028444              0.028238              0.060843  0.045023  0.051094  0.433290  0.021151           0.041979                   0.047948  0.065534            0.035638                  0.012570  0.041285  0.032555\n",
            "37    0.029579  0.024834  0.028538              0.028232              0.060829  0.045037  0.051087  0.433179  0.021149           0.041978                   0.047977  0.065521            0.035628                  0.012589  0.041298  0.032546\n",
            "38    0.029574  0.024881  0.028549              0.028340              0.060821  0.045023  0.051080  0.433047  0.021142           0.041999                   0.047970  0.065504            0.035624                  0.012615  0.041285  0.032545\n",
            "39    0.029586  0.024880  0.028554              0.028351              0.060834  0.045020  0.051080  0.433012  0.021152           0.041996                   0.047965  0.065498            0.035632                  0.012615  0.041282  0.032544\n",
            "40    0.029588  0.024887  0.028581              0.028352              0.060849  0.045018  0.051075  0.432960  0.021149           0.042009                   0.047960  0.065510            0.035629                  0.012615  0.041278  0.032540\n",
            "41    0.029588  0.024899  0.028578              0.028348              0.060853  0.045032  0.051069  0.432910  0.021147           0.042009                   0.047976  0.065538            0.035626                  0.012613  0.041277  0.032537\n",
            "42    0.029597  0.024897  0.028581              0.028358              0.060853  0.045029  0.051067  0.432878  0.021149           0.042015                   0.047978  0.065535            0.035629                  0.012625  0.041274  0.032534\n",
            "43    0.029597  0.024904  0.028617              0.028372              0.060846  0.045034  0.051062  0.432830  0.021150           0.042011                   0.047991  0.065533            0.035626                  0.012624  0.041269  0.032533\n",
            "44    0.029595  0.024924  0.028617              0.028385              0.060841  0.045035  0.051066  0.432799  0.021150           0.042014                   0.047992  0.065532            0.035624                  0.012623  0.041272  0.032531\n",
            "45    0.029595  0.024927  0.028618              0.028392              0.060859  0.045034  0.051063  0.432769  0.021149           0.042012                   0.047991  0.065531            0.035630                  0.012622  0.041276  0.032530\n",
            "46    0.029593  0.024926  0.028617              0.028391              0.060857  0.045031  0.051066  0.432744  0.021148           0.042028                   0.047997  0.065532            0.035633                  0.012632  0.041277  0.032528\n",
            "47    0.029593  0.024928  0.028617              0.028394              0.060858  0.045031  0.051063  0.432721  0.021148           0.042026                   0.047999  0.065541            0.035643                  0.012632  0.041277  0.032528\n",
            "\n",
            "FEVD for M2_UK\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.183179  0.000074  0.009563              0.011205              0.001750  0.003364  0.000003  0.005004  0.785858           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.142289  0.005023  0.007773              0.102354              0.010074  0.004086  0.000077  0.027301  0.611383           0.002017                   0.030239  0.005386            0.000016                  0.007882  0.003612  0.040487\n",
            "2     0.124510  0.012455  0.010487              0.089940              0.018795  0.003887  0.002769  0.050601  0.531609           0.016431                   0.030396  0.004678            0.033380                  0.010095  0.022834  0.037132\n",
            "3     0.113062  0.014869  0.009528              0.084966              0.025185  0.014382  0.006696  0.047493  0.493579           0.019244                   0.028693  0.015677            0.030207                  0.014719  0.048064  0.033636\n",
            "4     0.111037  0.027736  0.009384              0.082676              0.035684  0.013397  0.011425  0.044831  0.451624           0.028620                   0.026312  0.015816            0.027425                  0.020366  0.044457  0.049209\n",
            "5     0.098466  0.025658  0.076758              0.076258              0.042514  0.013448  0.010125  0.045554  0.404049           0.026970                   0.031083  0.014012            0.025329                  0.019287  0.046620  0.043869\n",
            "6     0.095257  0.031909  0.074196              0.079119              0.042061  0.014071  0.012636  0.044914  0.392599           0.026816                   0.030183  0.013631            0.031913                  0.018861  0.048327  0.043507\n",
            "7     0.090613  0.030376  0.069617              0.075759              0.043486  0.027983  0.011701  0.044501  0.373514           0.026772                   0.028094  0.012626            0.031078                  0.030559  0.047244  0.056077\n",
            "8     0.085947  0.028934  0.066856              0.078641              0.041586  0.026924  0.016551  0.042749  0.355769           0.037727                   0.026784  0.015652            0.033409                  0.037870  0.047761  0.056840\n",
            "9     0.082749  0.028929  0.072437              0.077792              0.040171  0.028155  0.017264  0.047007  0.351464           0.037497                   0.025784  0.015111            0.033012                  0.037984  0.046035  0.058610\n",
            "10    0.082353  0.029937  0.072393              0.077607              0.040091  0.029958  0.017694  0.046614  0.348523           0.037201                   0.025678  0.015504            0.034258                  0.037949  0.046063  0.058176\n",
            "11    0.082300  0.029405  0.070652              0.083519              0.040338  0.031025  0.017485  0.062730  0.333155           0.036282                   0.024540  0.014884            0.032724                  0.039153  0.044192  0.057617\n",
            "12    0.083061  0.028965  0.069535              0.081790              0.039454  0.030334  0.017244  0.061461  0.331746           0.035466                   0.032897  0.015568            0.032665                  0.038462  0.044158  0.057195\n",
            "13    0.081860  0.030525  0.069050              0.080645              0.039785  0.031059  0.018035  0.060577  0.326796           0.036944                   0.032500  0.015703            0.034603                  0.038102  0.047472  0.056343\n",
            "14    0.080525  0.030849  0.068651              0.080457              0.039168  0.031018  0.017890  0.060585  0.322227           0.041096                   0.034274  0.019135            0.034257                  0.037727  0.046697  0.055443\n",
            "15    0.079819  0.031834  0.070161              0.080305              0.038828  0.030758  0.018433  0.060183  0.320179           0.041802                   0.034436  0.020100            0.034200                  0.037450  0.046323  0.055189\n",
            "16    0.079535  0.031685  0.071661              0.080253              0.038554  0.030962  0.018737  0.060327  0.317767           0.042663                   0.034882  0.020026            0.033909                  0.037148  0.046172  0.055718\n",
            "17    0.078933  0.031418  0.071104              0.079611              0.038291  0.031500  0.020362  0.060502  0.316311           0.042620                   0.036389  0.020050            0.034684                  0.037125  0.045804  0.055296\n",
            "18    0.078411  0.031428  0.070905              0.078680              0.038805  0.031130  0.020700  0.060867  0.315033           0.045902                   0.036136  0.019913            0.035006                  0.036860  0.045567  0.054657\n",
            "19    0.078109  0.031449  0.070709              0.078392              0.038700  0.031034  0.021171  0.060632  0.313573           0.047119                   0.036120  0.021173            0.034887                  0.036985  0.045544  0.054404\n",
            "20    0.077953  0.031355  0.071587              0.078133              0.038586  0.030947  0.021869  0.060483  0.313018           0.046963                   0.036217  0.021204            0.034824                  0.037139  0.045419  0.054305\n",
            "21    0.077708  0.031271  0.071699              0.077974              0.038435  0.031362  0.021984  0.060247  0.311799           0.047057                   0.036198  0.021202            0.035061                  0.037013  0.045512  0.055479\n",
            "22    0.077560  0.031208  0.071674              0.077828              0.038552  0.031424  0.021975  0.060296  0.311326           0.047054                   0.036714  0.021194            0.034993                  0.036943  0.045880  0.055380\n",
            "23    0.077420  0.031208  0.071532              0.077817              0.038828  0.031361  0.022238  0.060193  0.311087           0.046967                   0.036860  0.021192            0.034933                  0.037130  0.045792  0.055442\n",
            "24    0.077280  0.031703  0.071890              0.077782              0.038775  0.031390  0.022202  0.060098  0.310590           0.046992                   0.036946  0.021243            0.034874                  0.037084  0.045800  0.055351\n",
            "25    0.077091  0.031845  0.071922              0.077732              0.038928  0.031498  0.022176  0.060475  0.309919           0.047405                   0.036918  0.021199            0.034795                  0.037040  0.045754  0.055305\n",
            "26    0.076991  0.031797  0.072306              0.077602              0.038901  0.031472  0.022355  0.060385  0.309439           0.047336                   0.037010  0.021203            0.035004                  0.037149  0.045683  0.055368\n",
            "27    0.076876  0.031749  0.072909              0.077536              0.038956  0.031498  0.022356  0.060512  0.308992           0.047316                   0.037083  0.021201            0.034953                  0.037101  0.045674  0.055289\n",
            "28    0.076787  0.031925  0.072818              0.077460              0.039325  0.031452  0.022410  0.060418  0.308518           0.047245                   0.037194  0.021402            0.034982                  0.037095  0.045650  0.055319\n",
            "29    0.076664  0.031892  0.072883              0.077469              0.039266  0.031402  0.022442  0.060673  0.308016           0.047429                   0.037260  0.021610            0.034972                  0.037085  0.045616  0.055322\n",
            "30    0.076601  0.031861  0.072842              0.077867              0.039266  0.031406  0.022422  0.060621  0.307816           0.047390                   0.037265  0.021589            0.035057                  0.037063  0.045573  0.055361\n",
            "31    0.076523  0.031897  0.072791              0.077896              0.039228  0.031402  0.022526  0.060594  0.307693           0.047341                   0.037386  0.021707            0.035069                  0.037058  0.045574  0.055316\n",
            "32    0.076466  0.031886  0.072847              0.077824              0.039220  0.031573  0.022696  0.060532  0.307389           0.047339                   0.037509  0.021685            0.035089                  0.037021  0.045585  0.055338\n",
            "33    0.076464  0.031931  0.072967              0.077869              0.039310  0.031586  0.022705  0.060485  0.307246           0.047311                   0.037486  0.021694            0.035079                  0.036996  0.045549  0.055322\n",
            "34    0.076436  0.031919  0.072947              0.077825              0.039344  0.031610  0.022693  0.060501  0.307164           0.047292                   0.037512  0.021797            0.035098                  0.037025  0.045548  0.055289\n",
            "35    0.076367  0.031902  0.072884              0.077764              0.039311  0.031738  0.022820  0.060449  0.306898           0.047386                   0.037635  0.021875            0.035226                  0.036999  0.045506  0.055241\n",
            "36    0.076338  0.031910  0.072859              0.077903              0.039291  0.031753  0.022867  0.060405  0.306832           0.047375                   0.037610  0.021964            0.035211                  0.036973  0.045490  0.055218\n",
            "37    0.076323  0.031902  0.072840              0.077920              0.039281  0.031772  0.022862  0.060391  0.306832           0.047364                   0.037636  0.021973            0.035241                  0.036982  0.045478  0.055204\n",
            "38    0.076285  0.031912  0.072834              0.077883              0.039306  0.031822  0.022883  0.060364  0.306685           0.047343                   0.037774  0.021969            0.035322                  0.036963  0.045467  0.055188\n",
            "39    0.076231  0.031986  0.072873              0.077927              0.039339  0.031833  0.022893  0.060326  0.306510           0.047338                   0.037749  0.022170            0.035292                  0.036930  0.045430  0.055173\n",
            "40    0.076189  0.032052  0.072848              0.077881              0.039315  0.031821  0.022882  0.060297  0.306398           0.047316                   0.037821  0.022288            0.035400                  0.036933  0.045418  0.055139\n",
            "41    0.076165  0.032043  0.072821              0.077889              0.039301  0.031859  0.022911  0.060269  0.306314           0.047298                   0.037925  0.022279            0.035490                  0.036926  0.045398  0.055113\n",
            "42    0.076123  0.032068  0.072818              0.078001              0.039287  0.031890  0.022911  0.060244  0.306313           0.047297                   0.037904  0.022297            0.035472                  0.036907  0.045374  0.055094\n",
            "43    0.076102  0.032068  0.072791              0.077985              0.039277  0.031878  0.022919  0.060247  0.306265           0.047285                   0.037946  0.022340            0.035541                  0.036900  0.045368  0.055088\n",
            "44    0.076069  0.032063  0.072766              0.077955              0.039279  0.031909  0.022967  0.060239  0.306147           0.047270                   0.038071  0.022337            0.035616                  0.036889  0.045350  0.055074\n",
            "45    0.076031  0.032068  0.072824              0.077943              0.039261  0.031913  0.022992  0.060216  0.306130           0.047278                   0.038070  0.022380            0.035598                  0.036870  0.045329  0.055098\n",
            "46    0.076005  0.032073  0.072867              0.077919              0.039248  0.031902  0.022991  0.060195  0.306045           0.047262                   0.038133  0.022411            0.035676                  0.036869  0.045313  0.055093\n",
            "47    0.075978  0.032069  0.072839              0.077919              0.039234  0.031949  0.023007  0.060198  0.305965           0.047253                   0.038208  0.022406            0.035730                  0.036876  0.045295  0.055074\n",
            "\n",
            "FEVD for Stock Market News\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.001215  0.043376  0.000116              0.000516              0.009100  0.000006  0.002300  0.000999  0.006585           0.935786                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.001296  0.042984  0.009685              0.022989              0.008469  0.021880  0.012438  0.012512  0.006050           0.819333                   0.005452  0.000595            0.002411                  0.023947  0.001205  0.008754\n",
            "2     0.002157  0.044002  0.015634              0.021452              0.010329  0.037095  0.036463  0.023445  0.009519           0.736687                   0.008705  0.012050            0.004766                  0.024396  0.005305  0.007996\n",
            "3     0.002260  0.041505  0.042230              0.023548              0.010382  0.034307  0.038346  0.036120  0.022992           0.682015                   0.008320  0.012821            0.004429                  0.026324  0.006567  0.007837\n",
            "4     0.007223  0.038054  0.040932              0.063761              0.013123  0.037670  0.038332  0.033106  0.023097           0.624429                   0.019809  0.011697            0.004402                  0.025190  0.006937  0.012237\n",
            "5     0.007021  0.037496  0.048707              0.060561              0.013464  0.035889  0.038103  0.031871  0.022056           0.593152                   0.021038  0.011553            0.008626                  0.033215  0.015632  0.021617\n",
            "6     0.006947  0.038253  0.054725              0.063639              0.014472  0.049198  0.036115  0.039345  0.020958           0.562212                   0.020556  0.010943            0.008171                  0.034033  0.015573  0.024861\n",
            "7     0.007448  0.045976  0.052839              0.062372              0.033099  0.049266  0.034191  0.040517  0.020232           0.530187                   0.022136  0.014111            0.007689                  0.036159  0.014651  0.029127\n",
            "8     0.012378  0.044698  0.050462              0.068881              0.031457  0.052478  0.035446  0.038879  0.019582           0.509098                   0.029684  0.014985            0.007833                  0.042342  0.013977  0.027821\n",
            "9     0.013752  0.045540  0.049579              0.068955              0.032383  0.051543  0.035199  0.041337  0.022601           0.501086                   0.029528  0.014712            0.010051                  0.042400  0.013780  0.027556\n",
            "10    0.013859  0.044685  0.050598              0.067632              0.034243  0.052514  0.034561  0.040782  0.023820           0.497633                   0.029172  0.017321            0.010766                  0.041622  0.013715  0.027077\n",
            "11    0.013844  0.044745  0.052983              0.067296              0.034256  0.051840  0.034215  0.041551  0.023551           0.491369                   0.031348  0.018306            0.013010                  0.041124  0.013591  0.026970\n",
            "12    0.014270  0.045544  0.052596              0.066750              0.034460  0.052875  0.034438  0.043060  0.023328           0.487757                   0.032742  0.018115            0.012861                  0.040652  0.013536  0.027017\n",
            "13    0.014564  0.044953  0.056515              0.065922              0.034278  0.052154  0.034440  0.042767  0.023241           0.481502                   0.035622  0.019185            0.014575                  0.040033  0.013645  0.026607\n",
            "14    0.014355  0.045205  0.057962              0.065143              0.033827  0.052125  0.037570  0.042251  0.023862           0.472632                   0.038127  0.018874            0.014306                  0.041134  0.015259  0.027367\n",
            "15    0.014482  0.045370  0.059247              0.064655              0.034163  0.052838  0.037772  0.043322  0.023702           0.469552                   0.037877  0.018771            0.015112                  0.040831  0.015145  0.027161\n",
            "16    0.014404  0.047509  0.059147              0.064507              0.033975  0.052625  0.037795  0.043162  0.023624           0.467058                   0.037808  0.018669            0.015058                  0.041660  0.015080  0.027919\n",
            "17    0.014511  0.047612  0.059088              0.064742              0.033884  0.052453  0.037724  0.043026  0.023629           0.465956                   0.037779  0.018616            0.015040                  0.042948  0.015136  0.027854\n",
            "18    0.014512  0.048514  0.059203              0.064343              0.033692  0.052124  0.038064  0.042758  0.024236           0.463150                   0.038970  0.019237            0.015023                  0.043352  0.015143  0.027679\n",
            "19    0.014577  0.048444  0.059393              0.064333              0.033646  0.053359  0.037941  0.042564  0.024703           0.460973                   0.038775  0.020020            0.015358                  0.043224  0.015116  0.027575\n",
            "20    0.014543  0.048332  0.059237              0.064158              0.033662  0.053212  0.037857  0.042857  0.024756           0.459653                   0.039572  0.020074            0.015418                  0.043245  0.015076  0.028348\n",
            "21    0.014522  0.048589  0.059040              0.064040              0.033704  0.053049  0.038294  0.043246  0.025061           0.458014                   0.039428  0.020664            0.015489                  0.043380  0.015128  0.028352\n",
            "22    0.014544  0.048551  0.059203              0.063939              0.033657  0.052978  0.038663  0.043233  0.025023           0.457286                   0.039579  0.020868            0.015590                  0.043313  0.015142  0.028431\n",
            "23    0.014574  0.048522  0.059553              0.063839              0.033851  0.052885  0.038605  0.043442  0.024967           0.456423                   0.039572  0.020835            0.015787                  0.043220  0.015271  0.028653\n",
            "24    0.014577  0.048493  0.059888              0.063748              0.033803  0.052895  0.038592  0.043381  0.024995           0.455883                   0.039939  0.020841            0.015770                  0.043182  0.015287  0.028726\n",
            "25    0.014618  0.049077  0.060001              0.063760              0.034206  0.053211  0.038504  0.043292  0.024937           0.454847                   0.039869  0.020900            0.015736                  0.043080  0.015304  0.028658\n",
            "26    0.014631  0.049512  0.060098              0.063945              0.034322  0.053209  0.038403  0.043156  0.024858           0.453396                   0.040735  0.021195            0.015719                  0.042993  0.015262  0.028567\n",
            "27    0.014658  0.049472  0.060081              0.063905              0.034292  0.053352  0.038403  0.043118  0.024982           0.453131                   0.040801  0.021255            0.015715                  0.042957  0.015323  0.028554\n",
            "28    0.014683  0.049532  0.060379              0.064062              0.034474  0.053277  0.038346  0.043074  0.024939           0.452328                   0.040778  0.021236            0.015771                  0.042920  0.015565  0.028636\n",
            "29    0.014700  0.049494  0.060321              0.064033              0.034442  0.053297  0.038311  0.043050  0.025102           0.451906                   0.040739  0.021297            0.015804                  0.043186  0.015551  0.028767\n",
            "30    0.014783  0.049522  0.060252              0.063977              0.034428  0.053229  0.038352  0.043014  0.025070           0.451340                   0.041073  0.021649            0.015809                  0.043173  0.015534  0.028794\n",
            "31    0.014781  0.049468  0.060204              0.064292              0.034404  0.053282  0.038311  0.043104  0.025332           0.450812                   0.041056  0.021629            0.015793                  0.043123  0.015553  0.028854\n",
            "32    0.014783  0.049444  0.060190              0.064251              0.034382  0.053317  0.038301  0.043069  0.025372           0.450477                   0.041168  0.021777            0.015838                  0.043188  0.015543  0.028899\n",
            "33    0.014780  0.049504  0.060163              0.064234              0.034378  0.053395  0.038308  0.043072  0.025382           0.450283                   0.041205  0.021794            0.015918                  0.043166  0.015535  0.028884\n",
            "34    0.014805  0.049578  0.060248              0.064230              0.034463  0.053376  0.038310  0.043043  0.025448           0.449958                   0.041185  0.021828            0.015915                  0.043151  0.015532  0.028930\n",
            "35    0.014797  0.049593  0.060291              0.064195              0.034461  0.053351  0.038297  0.043021  0.025522           0.449701                   0.041169  0.021822            0.016173                  0.043142  0.015544  0.028920\n",
            "36    0.014796  0.049568  0.060266              0.064162              0.034450  0.053335  0.038279  0.043030  0.025509           0.449475                   0.041476  0.021816            0.016218                  0.043121  0.015590  0.028908\n",
            "37    0.014785  0.049696  0.060391              0.064137              0.034466  0.053418  0.038292  0.043078  0.025557           0.449112                   0.041452  0.021834            0.016212                  0.043086  0.015582  0.028900\n",
            "38    0.014777  0.049716  0.060414              0.064094              0.034446  0.053442  0.038284  0.043049  0.025623           0.448800                   0.041591  0.021938            0.016251                  0.043068  0.015597  0.028912\n",
            "39    0.014782  0.049705  0.060403              0.064107              0.034437  0.053467  0.038280  0.043056  0.025626           0.448619                   0.041694  0.021931            0.016340                  0.043055  0.015598  0.028901\n",
            "40    0.014774  0.049729  0.060443              0.064105              0.034419  0.053474  0.038283  0.043032  0.025764           0.448397                   0.041675  0.022008            0.016334                  0.043031  0.015608  0.028924\n",
            "41    0.014782  0.049750  0.060481              0.064090              0.034407  0.053450  0.038271  0.043018  0.025807           0.448211                   0.041702  0.022036            0.016433                  0.043012  0.015601  0.028949\n",
            "42    0.014778  0.049731  0.060483              0.064074              0.034393  0.053441  0.038286  0.043008  0.025816           0.448072                   0.041828  0.022033            0.016504                  0.043011  0.015599  0.028942\n",
            "43    0.014782  0.049742  0.060491              0.064064              0.034378  0.053457  0.038303  0.042991  0.026073           0.447822                   0.041813  0.022027            0.016497                  0.042992  0.015596  0.028972\n",
            "44    0.014776  0.049750  0.060535              0.064059              0.034371  0.053441  0.038297  0.042989  0.026086           0.447645                   0.041876  0.022063            0.016566                  0.042983  0.015590  0.028975\n",
            "45    0.014771  0.049733  0.060511              0.064039              0.034360  0.053472  0.038342  0.042989  0.026124           0.447467                   0.041939  0.022054            0.016652                  0.043001  0.015584  0.028964\n",
            "46    0.014765  0.049752  0.060569              0.064033              0.034359  0.053508  0.038351  0.042975  0.026181           0.447312                   0.041945  0.022053            0.016647                  0.042986  0.015580  0.028984\n",
            "47    0.014762  0.049744  0.060602              0.064044              0.034351  0.053494  0.038341  0.042963  0.026207           0.447188                   0.041961  0.022050            0.016736                  0.042981  0.015576  0.028999\n",
            "\n",
            "FEVD for Economic Development News\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.004845  0.007905  0.000082              0.001545              0.016048  0.000126  0.001254  0.000007  0.000932           0.017987                   0.949268  0.000000            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.003892  0.019785  0.010708              0.001079              0.049008  0.025294  0.003108  0.000066  0.010237           0.014635                   0.838211  0.000225            0.000408                  0.005771  0.002502  0.015072\n",
            "2     0.003943  0.017230  0.012558              0.012972              0.062420  0.043816  0.004776  0.004397  0.022560           0.020046                   0.749589  0.001727            0.005934                  0.016151  0.004770  0.017110\n",
            "3     0.003580  0.017945  0.016491              0.011842              0.063448  0.040600  0.014187  0.003882  0.020428           0.022257                   0.662175  0.004495            0.041463                  0.014277  0.040470  0.022462\n",
            "4     0.007378  0.019904  0.043820              0.021853              0.065117  0.035679  0.016299  0.005737  0.025509           0.020712                   0.588207  0.015182            0.054459                  0.019182  0.041315  0.019647\n",
            "5     0.007777  0.022326  0.044252              0.034362              0.062263  0.034706  0.018122  0.009784  0.024669           0.021800                   0.562402  0.014529            0.063779                  0.018684  0.041272  0.019273\n",
            "6     0.008408  0.021354  0.045855              0.035485              0.061381  0.033285  0.023202  0.013877  0.025138           0.020910                   0.540111  0.014027            0.075899                  0.022221  0.040195  0.018652\n",
            "7     0.014186  0.020554  0.045865              0.034314              0.059752  0.033402  0.022509  0.013367  0.027997           0.021216                   0.526778  0.013970            0.084471                  0.021899  0.039765  0.019955\n",
            "8     0.023387  0.025513  0.046369              0.032337              0.065750  0.031976  0.032186  0.012596  0.026542           0.020172                   0.496155  0.029776            0.079481                  0.020612  0.037422  0.019726\n",
            "9     0.023780  0.026539  0.045037              0.032424              0.064264  0.037236  0.034795  0.013185  0.025738           0.020926                   0.485599  0.029096            0.085245                  0.020870  0.036174  0.019093\n",
            "10    0.023316  0.026041  0.044517              0.032353              0.067167  0.039735  0.034353  0.012910  0.025954           0.020926                   0.479902  0.029512            0.084197                  0.023172  0.035645  0.020299\n",
            "11    0.025413  0.025361  0.043812              0.036593              0.065921  0.039368  0.033428  0.013608  0.034746           0.022287                   0.465993  0.028810            0.084168                  0.022836  0.036909  0.020747\n",
            "12    0.025944  0.024515  0.043982              0.035761              0.064288  0.038360  0.037365  0.013190  0.035621           0.021879                   0.462061  0.028380            0.085121                  0.026849  0.035951  0.020734\n",
            "13    0.025370  0.028256  0.043171              0.039999              0.063250  0.038809  0.041265  0.012896  0.037748           0.021387                   0.449547  0.027679            0.084979                  0.029809  0.035670  0.020164\n",
            "14    0.024866  0.028080  0.042164              0.046222              0.062311  0.037895  0.040944  0.013238  0.043491           0.020914                   0.441901  0.029223            0.083217                  0.029925  0.035805  0.019804\n",
            "15    0.024334  0.027602  0.041253              0.046658              0.061065  0.039987  0.040790  0.014559  0.042771           0.021630                   0.433306  0.028595            0.092004                  0.030698  0.035125  0.019623\n",
            "16    0.024413  0.027127  0.041569              0.046980              0.060254  0.043913  0.040511  0.014314  0.043449           0.021429                   0.426649  0.029774            0.094125                  0.030425  0.035338  0.019729\n",
            "17    0.024284  0.028023  0.043056              0.046752              0.059906  0.044319  0.040002  0.014183  0.045255           0.022353                   0.420479  0.029786            0.095166                  0.030419  0.035437  0.020582\n",
            "18    0.023955  0.027859  0.043134              0.047627              0.059124  0.043777  0.039584  0.014057  0.044678           0.022053                   0.417256  0.029461            0.099775                  0.032110  0.035245  0.020306\n",
            "19    0.023525  0.027725  0.042384              0.052584              0.058455  0.044524  0.038885  0.014301  0.047713           0.022514                   0.413142  0.029377            0.098482                  0.031823  0.034625  0.019941\n",
            "20    0.023177  0.030652  0.042280              0.054241              0.057621  0.044526  0.038553  0.014109  0.049443           0.022183                   0.407178  0.031441            0.099293                  0.031431  0.034129  0.019745\n",
            "21    0.023673  0.030794  0.042119              0.053675              0.057357  0.044454  0.038526  0.014297  0.048872           0.022167                   0.406274  0.031127            0.101451                  0.031608  0.033928  0.019679\n",
            "22    0.024193  0.030627  0.042890              0.053607              0.057069  0.044131  0.038884  0.014311  0.049581           0.023949                   0.403640  0.031501            0.100753                  0.031812  0.033534  0.019518\n",
            "23    0.023888  0.030405  0.045853              0.054205              0.056362  0.043570  0.038702  0.014138  0.052803           0.024557                   0.398550  0.031397            0.100732                  0.031412  0.033124  0.020302\n",
            "24    0.023686  0.030216  0.046003              0.053758              0.055997  0.043333  0.039053  0.014290  0.052348           0.024347                   0.397668  0.031375            0.103156                  0.031798  0.032791  0.020182\n",
            "25    0.023467  0.030073  0.045970              0.054723              0.055360  0.043636  0.040080  0.014657  0.055847           0.024076                   0.394300  0.031098            0.102221                  0.031806  0.032419  0.020266\n",
            "26    0.023378  0.030446  0.046906              0.054876              0.054948  0.043732  0.039821  0.014553  0.058507           0.023952                   0.391485  0.030905            0.102021                  0.031727  0.032154  0.020588\n",
            "27    0.023230  0.030269  0.046960              0.054498              0.054565  0.043652  0.040063  0.015024  0.058099           0.023944                   0.390467  0.030911            0.103286                  0.032490  0.031983  0.020559\n",
            "28    0.023113  0.030800  0.047210              0.054453              0.054155  0.044141  0.040497  0.014932  0.059603           0.024138                   0.388365  0.031219            0.102720                  0.032400  0.031749  0.020504\n",
            "29    0.022945  0.031823  0.048816              0.054198              0.053814  0.043940  0.040203  0.014889  0.060188           0.023965                   0.385576  0.031327            0.103635                  0.032431  0.031554  0.020696\n",
            "30    0.022803  0.031866  0.048569              0.053837              0.053480  0.043684  0.040331  0.014812  0.059848           0.024395                   0.385019  0.031158            0.105312                  0.032973  0.031344  0.020567\n",
            "31    0.022678  0.031936  0.048810              0.053919              0.053185  0.043813  0.040487  0.014877  0.060883           0.024521                   0.384216  0.031042            0.104760                  0.033081  0.031171  0.020619\n",
            "32    0.022559  0.032419  0.049637              0.054391              0.053011  0.043884  0.040314  0.014829  0.061690           0.024424                   0.382391  0.031034            0.104797                  0.032927  0.031013  0.020680\n",
            "33    0.022455  0.032292  0.049686              0.054108              0.052744  0.043764  0.040381  0.015159  0.061365           0.024444                   0.382640  0.030883            0.105241                  0.033303  0.030934  0.020600\n",
            "34    0.022347  0.032449  0.050498              0.054377              0.052504  0.043918  0.040647  0.015202  0.061898           0.024442                   0.381338  0.030754            0.104739                  0.033356  0.030770  0.020761\n",
            "35    0.022269  0.032798  0.051789              0.054523              0.052285  0.043718  0.040445  0.015122  0.062525           0.024317                   0.379416  0.030740            0.104799                  0.033314  0.030690  0.021248\n",
            "36    0.022211  0.032713  0.051589              0.054299              0.052068  0.043608  0.040532  0.015172  0.062265           0.024371                   0.379392  0.030678            0.105613                  0.033731  0.030590  0.021170\n",
            "37    0.022112  0.032758  0.051876              0.054636              0.051836  0.043871  0.040781  0.015220  0.063145           0.024271                   0.378281  0.030550            0.105228                  0.033751  0.030454  0.021229\n",
            "38    0.022012  0.033172  0.052443              0.055137              0.051689  0.043968  0.040595  0.015159  0.063753           0.024185                   0.376835  0.030527            0.105078                  0.033707  0.030344  0.021395\n",
            "39    0.021937  0.033078  0.052291              0.054968              0.051564  0.043872  0.040881  0.015362  0.063535           0.024102                   0.376570  0.030467            0.105613                  0.034187  0.030244  0.021328\n",
            "40    0.021863  0.033111  0.052792              0.055054              0.051420  0.044048  0.041133  0.015381  0.064003           0.024010                   0.375683  0.030352            0.105408                  0.034114  0.030153  0.021476\n",
            "41    0.021787  0.033356  0.053572              0.055094              0.051327  0.044054  0.040990  0.015336  0.064480           0.023932                   0.374336  0.030413            0.105459                  0.034187  0.030091  0.021588\n",
            "42    0.021722  0.033336  0.053459              0.054931              0.051181  0.043946  0.040990  0.015323  0.064293           0.023910                   0.374561  0.030394            0.106025                  0.034400  0.030002  0.021527\n",
            "43    0.021667  0.033476  0.053637              0.055125              0.051050  0.044122  0.041090  0.015366  0.064655           0.023843                   0.374048  0.030322            0.105747                  0.034384  0.029926  0.021542\n",
            "44    0.021594  0.033805  0.054042              0.055455              0.050983  0.044195  0.040982  0.015315  0.065117           0.023772                   0.372921  0.030361            0.105646                  0.034323  0.029837  0.021651\n",
            "45    0.021576  0.033737  0.053957              0.055325              0.050859  0.044119  0.041044  0.015377  0.064969           0.023728                   0.372946  0.030370            0.106075                  0.034533  0.029771  0.021613\n",
            "46    0.021539  0.033837  0.054235              0.055492              0.050795  0.044249  0.041225  0.015359  0.065271           0.023679                   0.372276  0.030302            0.105895                  0.034478  0.029689  0.021680\n",
            "47    0.021473  0.034143  0.054728              0.055587              0.050690  0.044242  0.041112  0.015315  0.065763           0.023612                   0.371190  0.030428            0.105836                  0.034470  0.029611  0.021801\n",
            "\n",
            "FEVD for FED News\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.011253  0.013397  0.007543              0.030056              0.029929  0.000942  0.000262  0.001016  0.010757           0.003906                   0.082655  0.808285            0.000000                  0.000000  0.000000  0.000000\n",
            "1     0.013720  0.025031  0.034705              0.035513              0.029556  0.001486  0.001510  0.004238  0.048331           0.003173                   0.068266  0.719579            0.000542                  0.002030  0.011704  0.000615\n",
            "2     0.013760  0.021904  0.047105              0.060889              0.067762  0.006232  0.011422  0.003717  0.045065           0.004176                   0.068944  0.629735            0.005678                  0.002379  0.010235  0.000997\n",
            "3     0.017080  0.020756  0.046019              0.071868              0.077404  0.005903  0.010873  0.004614  0.042295           0.004020                   0.064662  0.604275            0.007638                  0.003082  0.018042  0.001467\n",
            "4     0.022685  0.021590  0.060044              0.064521              0.069577  0.011690  0.024454  0.004275  0.042985           0.004441                   0.080737  0.535972            0.011500                  0.004180  0.038681  0.002670\n",
            "5     0.021090  0.037774  0.064993              0.057550              0.069333  0.043359  0.026274  0.005022  0.039596           0.009809                   0.077114  0.481817            0.012507                  0.016581  0.034524  0.002657\n",
            "6     0.020155  0.035568  0.062035              0.054757              0.066139  0.040874  0.024805  0.005048  0.037396           0.016979                   0.087232  0.466118            0.021052                  0.025372  0.033806  0.002664\n",
            "7     0.018534  0.044406  0.057038              0.052939              0.066762  0.064489  0.022783  0.008984  0.045173           0.025307                   0.081104  0.428130            0.024547                  0.025842  0.031064  0.002897\n",
            "8     0.017554  0.043962  0.055235              0.060870              0.064980  0.068120  0.021618  0.009636  0.044096           0.024744                   0.089659  0.413629            0.023979                  0.027129  0.031714  0.003077\n",
            "9     0.017278  0.043223  0.054757              0.061953              0.063925  0.066765  0.021969  0.009446  0.044199           0.029304                   0.089798  0.408673            0.025063                  0.028864  0.031515  0.003267\n",
            "10    0.016739  0.041782  0.052969              0.062873              0.062975  0.064697  0.022113  0.009153  0.052272           0.028305                   0.093704  0.394667            0.028656                  0.030615  0.030498  0.007984\n",
            "11    0.016059  0.040145  0.060582              0.063167              0.062063  0.067228  0.021949  0.009263  0.059785           0.027617                   0.091442  0.381537            0.029093                  0.030027  0.029341  0.010702\n",
            "12    0.016451  0.039883  0.061445              0.063244              0.060022  0.064993  0.024106  0.009193  0.057417           0.027078                   0.099246  0.371828            0.035182                  0.030261  0.028760  0.010889\n",
            "13    0.016041  0.040630  0.059966              0.065878              0.059269  0.067323  0.023871  0.008937  0.062093           0.026396                   0.101903  0.362428            0.034192                  0.029517  0.030407  0.011149\n",
            "14    0.016128  0.043385  0.061500              0.066606              0.058244  0.066782  0.023914  0.008747  0.063597           0.026630                   0.101248  0.356030            0.037320                  0.028900  0.029824  0.011145\n",
            "15    0.015795  0.042809  0.061784              0.067355              0.057038  0.065554  0.023339  0.008719  0.064359           0.026252                   0.102457  0.349609            0.044987                  0.028124  0.029017  0.012802\n",
            "16    0.017295  0.044424  0.062181              0.066145              0.057716  0.065598  0.023572  0.008551  0.063212           0.027740                   0.102119  0.346452            0.045900                  0.027683  0.028848  0.012562\n",
            "17    0.017071  0.046713  0.064152              0.065206              0.056684  0.064847  0.023234  0.009303  0.066110           0.027736                   0.100299  0.342085            0.047486                  0.027912  0.028419  0.012743\n",
            "18    0.016877  0.047433  0.063686              0.064606              0.055894  0.063793  0.022958  0.009159  0.065279           0.027547                   0.104528  0.337118            0.051054                  0.028507  0.028972  0.012588\n",
            "19    0.017455  0.047021  0.062751              0.064077              0.055045  0.065728  0.022604  0.010514  0.069184           0.027370                   0.105665  0.332362            0.050790                  0.028511  0.028526  0.012396\n",
            "20    0.017649  0.047772  0.063418              0.064077              0.054371  0.065706  0.022417  0.010488  0.072631           0.027451                   0.104997  0.329112            0.050647                  0.028217  0.028254  0.012793\n",
            "21    0.017749  0.047383  0.063183              0.063282              0.053560  0.065272  0.023538  0.011401  0.071797           0.027165                   0.105727  0.324203            0.054116                  0.029558  0.028775  0.013289\n",
            "22    0.017442  0.046539  0.065395              0.063126              0.052895  0.064144  0.024481  0.011194  0.074089           0.029032                   0.106004  0.318313            0.054048                  0.031011  0.028252  0.014035\n",
            "23    0.017568  0.046640  0.067152              0.062976              0.052334  0.063480  0.024233  0.011073  0.075787           0.029238                   0.104982  0.314941            0.055813                  0.030783  0.027950  0.015052\n",
            "24    0.017413  0.046431  0.066602              0.062485              0.051899  0.062920  0.024528  0.011350  0.075120           0.029218                   0.107449  0.312180            0.058662                  0.031096  0.027707  0.014941\n",
            "25    0.017265  0.046422  0.066267              0.062425              0.051300  0.064228  0.025464  0.011861  0.076927           0.028878                   0.108286  0.308537            0.058189                  0.031422  0.027700  0.014829\n",
            "26    0.017370  0.047280  0.069044              0.063029              0.051036  0.064607  0.025363  0.011724  0.077548           0.028585                   0.107699  0.304985            0.058081                  0.031059  0.027381  0.015210\n",
            "27    0.017328  0.046922  0.069258              0.062574              0.050678  0.064654  0.025405  0.011836  0.076976           0.028368                   0.108540  0.302740            0.060378                  0.031536  0.027210  0.015598\n",
            "28    0.017337  0.047256  0.069700              0.062345              0.050440  0.065319  0.025584  0.011769  0.077324           0.028270                   0.108863  0.301116            0.060501                  0.031458  0.027039  0.015680\n",
            "29    0.017210  0.047887  0.070790              0.062622              0.050062  0.065400  0.025386  0.011725  0.078506           0.028066                   0.108050  0.299001            0.061282                  0.031332  0.026835  0.015847\n",
            "30    0.017077  0.048223  0.070586              0.062322              0.049824  0.064923  0.025461  0.011641  0.077933           0.028201                   0.110340  0.296858            0.062329                  0.031909  0.026626  0.015748\n",
            "31    0.016980  0.048073  0.070340              0.062455              0.049521  0.065196  0.025843  0.011977  0.079085           0.028087                   0.110617  0.295018            0.062069                  0.032482  0.026461  0.015797\n",
            "32    0.016872  0.048316  0.071159              0.063060              0.049575  0.065123  0.025693  0.011920  0.080138           0.027914                   0.110071  0.293474            0.061832                  0.032373  0.026404  0.016076\n",
            "33    0.016771  0.048093  0.070965              0.062690              0.049286  0.064790  0.026068  0.012196  0.079665           0.027771                   0.110573  0.291927            0.063450                  0.033421  0.026354  0.015982\n",
            "34    0.016671  0.047854  0.071417              0.062793              0.049180  0.064623  0.026381  0.012125  0.080049           0.027789                   0.111510  0.290182            0.063575                  0.033470  0.026245  0.016135\n",
            "35    0.016592  0.048166  0.072152              0.063147              0.049017  0.064376  0.026273  0.012073  0.080802           0.027648                   0.110928  0.288749            0.064142                  0.033382  0.026181  0.016371\n",
            "36    0.016532  0.048041  0.071863              0.062937              0.048809  0.064094  0.026260  0.012053  0.080504           0.027566                   0.112354  0.287515            0.065270                  0.033770  0.026064  0.016368\n",
            "37    0.016504  0.048215  0.071778              0.063227              0.048654  0.064380  0.026568  0.012269  0.080900           0.027433                   0.112629  0.286134            0.065134                  0.033884  0.025940  0.016351\n",
            "38    0.016433  0.048655  0.072405              0.063691              0.048635  0.064416  0.026477  0.012211  0.081530           0.027289                   0.112093  0.284843            0.065069                  0.033820  0.025829  0.016604\n",
            "39    0.016410  0.048536  0.072358              0.063436              0.048440  0.064210  0.026598  0.012299  0.081229           0.027179                   0.112667  0.283788            0.066246                  0.034249  0.025738  0.016615\n",
            "40    0.016370  0.048464  0.072585              0.063518              0.048331  0.064341  0.026742  0.012277  0.081431           0.027133                   0.113368  0.282621            0.066329                  0.034182  0.025661  0.016647\n",
            "41    0.016295  0.048848  0.073262              0.063619              0.048152  0.064299  0.026654  0.012226  0.082282           0.027011                   0.112866  0.281598            0.066439                  0.034094  0.025557  0.016798\n",
            "42    0.016237  0.048854  0.073169              0.063397              0.047981  0.064058  0.026652  0.012248  0.082018           0.026959                   0.114103  0.280684            0.067147                  0.034271  0.025461  0.016760\n",
            "43    0.016192  0.048834  0.073095              0.063597              0.047810  0.064182  0.026958  0.012352  0.082612           0.026885                   0.114305  0.279640            0.067083                  0.034325  0.025368  0.016764\n",
            "44    0.016127  0.049108  0.073726              0.063857              0.047716  0.064144  0.026890  0.012303  0.083238           0.026784                   0.113884  0.278744            0.066989                  0.034243  0.025296  0.016951\n",
            "45    0.016108  0.049015  0.073661              0.063677              0.047580  0.063989  0.027018  0.012350  0.083025           0.026711                   0.114311  0.278045            0.067845                  0.034503  0.025227  0.016936\n",
            "46    0.016062  0.048950  0.073744              0.063727              0.047481  0.064066  0.027254  0.012340  0.083274           0.026696                   0.114840  0.277109            0.067880                  0.034451  0.025150  0.016976\n",
            "47    0.016006  0.049212  0.074185              0.063862              0.047351  0.064067  0.027199  0.012300  0.083922           0.026613                   0.114453  0.276305            0.067969                  0.034383  0.025068  0.017106\n",
            "\n",
            "FEVD for Micro Finance News\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.005080  0.001121  0.003377              0.019414              0.004082  0.000198  0.005485  0.013348  0.004566           0.021602                   0.355809  0.052362            0.513556                  0.000000  0.000000  0.000000\n",
            "1     0.005831  0.001119  0.020141              0.014312              0.039036  0.003027  0.005322  0.009785  0.020168           0.015821                   0.331576  0.092108            0.406379                  0.008942  0.014606  0.011827\n",
            "2     0.005210  0.005044  0.017311              0.022739              0.043319  0.009901  0.004555  0.026000  0.032308           0.015642                   0.319090  0.078734            0.372465                  0.022817  0.014578  0.010288\n",
            "3     0.005500  0.004508  0.015408              0.025098              0.040945  0.015647  0.018184  0.023190  0.041623           0.029304                   0.304062  0.072521            0.350341                  0.022331  0.016284  0.015056\n",
            "4     0.008576  0.010228  0.028147              0.050882              0.039923  0.015097  0.019158  0.021261  0.055595           0.030870                   0.276222  0.066184            0.321773                  0.027510  0.014854  0.013723\n",
            "5     0.008295  0.009601  0.033643              0.048544              0.037354  0.014624  0.024539  0.023346  0.054638           0.029012                   0.258890  0.064622            0.324528                  0.036321  0.014063  0.017980\n",
            "6     0.008205  0.008616  0.030352              0.044925              0.035366  0.017212  0.036235  0.021174  0.049164           0.026403                   0.235784  0.058130            0.341631                  0.042020  0.028043  0.016741\n",
            "7     0.015399  0.009446  0.028886              0.045938              0.033412  0.018304  0.034443  0.024239  0.062333           0.026343                   0.230898  0.054271            0.322505                  0.041521  0.034342  0.017721\n",
            "8     0.015987  0.015758  0.032358              0.049276              0.035743  0.017200  0.036942  0.023640  0.072532           0.027740                   0.222932  0.056123            0.303957                  0.040594  0.032496  0.016721\n",
            "9     0.015425  0.015247  0.032052              0.053636              0.038470  0.024009  0.040708  0.023359  0.069417           0.028170                   0.223271  0.054228            0.294644                  0.039891  0.031330  0.016143\n",
            "10    0.014816  0.019696  0.031317              0.051766              0.039141  0.028977  0.042908  0.022557  0.081609           0.027491                   0.216553  0.052029            0.284326                  0.039312  0.030497  0.017003\n",
            "11    0.014376  0.022843  0.039193              0.050658              0.040495  0.028110  0.041390  0.023301  0.087843           0.026491                   0.208632  0.050179            0.278686                  0.040858  0.030275  0.016670\n",
            "12    0.013810  0.022572  0.039584              0.048970              0.038839  0.029153  0.041083  0.022172  0.083630           0.028797                   0.211565  0.048067            0.277312                  0.048474  0.028809  0.017163\n",
            "13    0.013476  0.025795  0.038818              0.056485              0.038713  0.030655  0.040919  0.021468  0.084019           0.028398                   0.208289  0.047285            0.269567                  0.050774  0.027885  0.017455\n",
            "14    0.013246  0.026830  0.037683              0.067836              0.037784  0.031925  0.039515  0.021194  0.086454           0.028061                   0.206122  0.046879            0.262549                  0.050121  0.026947  0.016857\n",
            "15    0.013149  0.026013  0.036596              0.066524              0.036716  0.031088  0.040777  0.023964  0.083821           0.027206                   0.207974  0.046503            0.264514                  0.052392  0.026389  0.016373\n",
            "16    0.013624  0.025973  0.037281              0.069418              0.036257  0.032956  0.040845  0.023989  0.087045           0.026529                   0.204849  0.046424            0.258970                  0.051398  0.026503  0.017938\n",
            "17    0.013441  0.027147  0.041149              0.068510              0.035507  0.032437  0.040307  0.023474  0.088692           0.026919                   0.200345  0.046572            0.256789                  0.052400  0.027265  0.019044\n",
            "18    0.013140  0.027146  0.041062              0.069511              0.034789  0.032120  0.040001  0.023248  0.086726           0.026333                   0.201075  0.045777            0.258489                  0.055244  0.026672  0.018668\n",
            "19    0.012837  0.026678  0.040912              0.075159              0.034132  0.033156  0.039219  0.023001  0.091311           0.026160                   0.200706  0.044734            0.252884                  0.054549  0.026056  0.018508\n",
            "20    0.012638  0.027822  0.042632              0.077283              0.033494  0.033412  0.038599  0.022670  0.094062           0.025764                   0.197995  0.044862            0.251021                  0.053881  0.025648  0.018218\n",
            "21    0.013379  0.027421  0.042284              0.076233              0.033013  0.033185  0.039219  0.022823  0.092829           0.025510                   0.200346  0.044291            0.251522                  0.054136  0.025699  0.018109\n",
            "22    0.013924  0.028563  0.044592              0.076000              0.032723  0.033328  0.040041  0.022877  0.094045           0.025234                   0.199108  0.044729            0.248079                  0.053370  0.025312  0.018071\n",
            "23    0.013836  0.029381  0.049049              0.075744              0.032292  0.032837  0.039532  0.022552  0.096153           0.024905                   0.196046  0.044519            0.246277                  0.053107  0.024947  0.018823\n",
            "24    0.013664  0.029467  0.048887              0.074988              0.031991  0.032694  0.039625  0.022387  0.095012           0.024816                   0.198346  0.044008            0.247184                  0.053643  0.024640  0.018649\n",
            "25    0.013473  0.029508  0.049680              0.075801              0.031560  0.033365  0.040225  0.022332  0.098364           0.024630                   0.197851  0.043511            0.243700                  0.053109  0.024311  0.018579\n",
            "26    0.013388  0.030658  0.051144              0.075549              0.031145  0.033715  0.039698  0.022036  0.100951           0.024496                   0.196550  0.043208            0.242035                  0.052686  0.023985  0.018754\n",
            "27    0.013358  0.030360  0.050742              0.074830              0.030839  0.033484  0.040185  0.022801  0.099988           0.024626                   0.198292  0.042791            0.242113                  0.053212  0.023752  0.018626\n",
            "28    0.013236  0.031255  0.051094              0.074804              0.030541  0.033762  0.041087  0.022654  0.101845           0.024559                   0.197516  0.042764            0.239798                  0.052813  0.023502  0.018771\n",
            "29    0.013132  0.032336  0.052904              0.074446              0.030294  0.033550  0.040710  0.022642  0.102577           0.024329                   0.195765  0.042839            0.239144                  0.052662  0.023356  0.019313\n",
            "30    0.013022  0.032293  0.052453              0.073789              0.030022  0.033428  0.041046  0.022548  0.101683           0.024498                   0.197363  0.042610            0.239739                  0.053227  0.023141  0.019139\n",
            "31    0.012905  0.032441  0.053315              0.073902              0.029784  0.033887  0.041288  0.022457  0.102989           0.024519                   0.197258  0.042235            0.237567                  0.053005  0.022934  0.019514\n",
            "32    0.012802  0.033255  0.054277              0.074091              0.029720  0.034093  0.040945  0.022380  0.103697           0.024340                   0.196248  0.042265            0.236722                  0.052717  0.022759  0.019690\n",
            "33    0.012727  0.033026  0.053938              0.073522              0.029544  0.033994  0.041343  0.022685  0.102958           0.024212                   0.198087  0.041994            0.236679                  0.053121  0.022630  0.019538\n",
            "34    0.012712  0.033494  0.054887              0.073626              0.029360  0.034610  0.041824  0.022600  0.103673           0.024024                   0.197353  0.041790            0.234952                  0.052936  0.022467  0.019692\n",
            "35    0.012624  0.033999  0.056451              0.073880              0.029336  0.034522  0.041530  0.022448  0.104199           0.023854                   0.196166  0.041706            0.234027                  0.052760  0.022389  0.020109\n",
            "36    0.012610  0.033839  0.056129              0.073456              0.029171  0.034464  0.041817  0.022433  0.103598           0.023813                   0.197276  0.041571            0.234396                  0.053163  0.022265  0.020000\n",
            "37    0.012525  0.033977  0.056863              0.073742              0.029036  0.035016  0.042076  0.022447  0.104389           0.023658                   0.196876  0.041309            0.232850                  0.052954  0.022116  0.020165\n",
            "38    0.012440  0.034547  0.057629              0.074128              0.029000  0.035151  0.041786  0.022292  0.105056           0.023518                   0.195981  0.041408            0.231936                  0.052758  0.021995  0.020375\n",
            "39    0.012392  0.034389  0.057389              0.073759              0.028891  0.035084  0.042114  0.022424  0.104535           0.023406                   0.196912  0.041287            0.232093                  0.053152  0.021896  0.020277\n",
            "40    0.012343  0.034572  0.057854              0.074047              0.028842  0.035523  0.042414  0.022392  0.105159           0.023277                   0.196351  0.041108            0.230964                  0.052932  0.021787  0.020436\n",
            "41    0.012276  0.034950  0.058761              0.074271              0.028831  0.035500  0.042182  0.022290  0.105734           0.023148                   0.195397  0.041226            0.230233                  0.052864  0.021707  0.020629\n",
            "42    0.012226  0.034851  0.058512              0.073933              0.028701  0.035404  0.042315  0.022279  0.105255           0.023088                   0.196396  0.041146            0.230628                  0.053116  0.021612  0.020536\n",
            "43    0.012170  0.035015  0.058874              0.074101              0.028644  0.035719  0.042485  0.022262  0.105776           0.022993                   0.196293  0.040999            0.229563                  0.052920  0.021537  0.020648\n",
            "44    0.012108  0.035469  0.059407              0.074290              0.028604  0.035838  0.042285  0.022149  0.106275           0.022881                   0.195577  0.041107            0.228971                  0.052810  0.021440  0.020790\n",
            "45    0.012083  0.035347  0.059227              0.074006              0.028494  0.035774  0.042385  0.022216  0.105865           0.022802                   0.196391  0.041045            0.229222                  0.053060  0.021359  0.020725\n",
            "46    0.012057  0.035543  0.059487              0.074261              0.028478  0.036136  0.042588  0.022170  0.106257           0.022720                   0.196058  0.040936            0.228338                  0.052888  0.021273  0.020811\n",
            "47    0.012006  0.035875  0.060088              0.074378              0.028422  0.036143  0.042411  0.022080  0.106763           0.022634                   0.195338  0.041068            0.227824                  0.052788  0.021199  0.020982\n",
            "\n",
            "FEVD for International Trade News\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.011166  0.002829  0.003051              0.051125              0.000085  0.002730  0.008681  0.084807  0.005622           0.007614                   0.167193  0.017813            0.138077                  0.499205  0.000000  0.000000\n",
            "1     0.045670  0.002045  0.008887              0.036838              0.028832  0.004164  0.011772  0.061272  0.025408           0.005475                   0.163586  0.030816            0.111293                  0.440989  0.006035  0.016918\n",
            "2     0.041128  0.023200  0.012965              0.036112              0.025878  0.005558  0.013390  0.055021  0.048048           0.007772                   0.165343  0.030676            0.109869                  0.401791  0.005434  0.017814\n",
            "3     0.043613  0.024489  0.012421              0.034619              0.027978  0.007556  0.013419  0.052844  0.059168           0.008600                   0.165554  0.032231            0.107293                  0.384812  0.006556  0.018849\n",
            "4     0.042724  0.023648  0.014458              0.035574              0.027089  0.007255  0.013421  0.050802  0.071326           0.008221                   0.164855  0.032559            0.104848                  0.367598  0.016504  0.019118\n",
            "5     0.043370  0.028269  0.013839              0.035093              0.033301  0.007192  0.012971  0.056281  0.075211           0.017009                   0.151737  0.034012            0.099014                  0.358391  0.015337  0.018972\n",
            "6     0.040691  0.030386  0.013292              0.039301              0.031111  0.008274  0.023007  0.052742  0.070606           0.015957                   0.152963  0.039652            0.095836                  0.343647  0.024758  0.017778\n",
            "7     0.038496  0.028686  0.017289              0.038425              0.036254  0.007832  0.026218  0.056890  0.075566           0.029176                   0.148205  0.038104            0.089809                  0.325082  0.026560  0.017410\n",
            "8     0.036548  0.027483  0.024526              0.036937              0.035079  0.014992  0.026431  0.057577  0.085490           0.028164                   0.144651  0.036948            0.085351                  0.308922  0.033306  0.017595\n",
            "9     0.039127  0.032458  0.023887              0.039516              0.036372  0.017482  0.029988  0.056024  0.083174           0.028327                   0.140850  0.035943            0.083065                  0.303807  0.032679  0.017301\n",
            "10    0.037275  0.042831  0.027679              0.040489              0.034716  0.019024  0.030816  0.055664  0.091494           0.028448                   0.134351  0.034241            0.081418                  0.289886  0.031418  0.020250\n",
            "11    0.036690  0.046785  0.028234              0.039715              0.035165  0.019923  0.030208  0.058970  0.091413           0.028034                   0.131812  0.033563            0.083218                  0.284353  0.031733  0.020185\n",
            "12    0.036038  0.046000  0.028192              0.038948              0.035825  0.019735  0.029846  0.057742  0.090219           0.031382                   0.130724  0.036964            0.086351                  0.279248  0.033020  0.019766\n",
            "13    0.036659  0.049154  0.028018              0.039069              0.035589  0.022162  0.029533  0.057316  0.089829           0.030906                   0.128988  0.036569            0.085438                  0.275876  0.032871  0.022025\n",
            "14    0.036144  0.052055  0.027519              0.045738              0.038544  0.021635  0.029055  0.056518  0.087686           0.031057                   0.128927  0.037805            0.083365                  0.270141  0.032246  0.021565\n",
            "15    0.035512  0.052104  0.027596              0.044870              0.037747  0.021412  0.028479  0.059711  0.085922           0.030430                   0.131905  0.039725            0.085202                  0.265620  0.032295  0.021470\n",
            "16    0.036333  0.051357  0.027190              0.044945              0.037477  0.022464  0.028410  0.058863  0.086891           0.029995                   0.131583  0.039802            0.083988                  0.262131  0.034194  0.024378\n",
            "17    0.035996  0.050880  0.026962              0.045258              0.037149  0.022741  0.029571  0.058245  0.086369           0.029803                   0.131055  0.040612            0.083876                  0.260650  0.036638  0.024194\n",
            "18    0.035518  0.050203  0.026727              0.047928              0.036960  0.022487  0.030792  0.057690  0.085872           0.029439                   0.131622  0.040611            0.083883                  0.259863  0.036141  0.024261\n",
            "19    0.035224  0.049723  0.027450              0.053018              0.036644  0.022495  0.031007  0.057061  0.087051           0.029102                   0.130408  0.040463            0.083100                  0.257350  0.035821  0.024083\n",
            "20    0.035417  0.049434  0.027294              0.053782              0.036414  0.022402  0.031116  0.057064  0.088225           0.028964                   0.130371  0.040305            0.083633                  0.256047  0.035597  0.023934\n",
            "21    0.036034  0.049638  0.027998              0.053230              0.036383  0.022486  0.033068  0.056800  0.087285           0.029379                   0.130385  0.039873            0.084748                  0.253401  0.035410  0.023881\n",
            "22    0.036364  0.049656  0.030351              0.052819              0.036082  0.023272  0.033740  0.056356  0.087745           0.029566                   0.129418  0.040165            0.084042                  0.251407  0.035117  0.023902\n",
            "23    0.036250  0.049658  0.033023              0.052485              0.035999  0.023125  0.033699  0.056183  0.087850           0.029389                   0.128912  0.039929            0.084327                  0.249869  0.035003  0.024298\n",
            "24    0.036167  0.049633  0.033006              0.052700              0.035876  0.023760  0.033608  0.055990  0.087546           0.029320                   0.129327  0.039923            0.084828                  0.249044  0.035057  0.024215\n",
            "25    0.035950  0.050224  0.033359              0.053521              0.035727  0.024057  0.033483  0.055714  0.088763           0.029170                   0.128575  0.040730            0.084293                  0.247376  0.034822  0.024236\n",
            "26    0.035781  0.051020  0.033583              0.053394              0.035525  0.024211  0.033304  0.055395  0.089469           0.029461                   0.128541  0.040904            0.084616                  0.246028  0.034642  0.024128\n",
            "27    0.035659  0.050779  0.033420              0.053172              0.035392  0.024104  0.033700  0.055187  0.089443           0.030000                   0.130033  0.040744            0.084899                  0.244929  0.034527  0.024013\n",
            "28    0.035511  0.051278  0.033914              0.053394              0.035304  0.024164  0.033673  0.054927  0.090295           0.030206                   0.129610  0.041151            0.084499                  0.243777  0.034366  0.023932\n",
            "29    0.035365  0.051643  0.033938              0.053184              0.035166  0.024059  0.033561  0.054961  0.090557           0.030114                   0.129518  0.041607            0.085016                  0.242947  0.034253  0.024111\n",
            "30    0.035232  0.051414  0.033777              0.052985              0.035030  0.024234  0.034213  0.054869  0.090310           0.029991                   0.130394  0.041637            0.085687                  0.242101  0.034093  0.024034\n",
            "31    0.035108  0.051189  0.034193              0.053492              0.034897  0.024309  0.034492  0.054628  0.091271           0.030058                   0.130116  0.041450            0.085301                  0.241078  0.033975  0.024441\n",
            "32    0.035124  0.051151  0.034221              0.053497              0.034804  0.024272  0.034445  0.054584  0.091538           0.029977                   0.130237  0.041395            0.085809                  0.240542  0.033885  0.024518\n",
            "33    0.035012  0.051153  0.034168              0.053308              0.034681  0.024455  0.034724  0.054760  0.091293           0.029872                   0.131116  0.041254            0.086057                  0.239904  0.033759  0.024484\n",
            "34    0.034890  0.051627  0.034586              0.053379              0.034598  0.024873  0.034855  0.054596  0.091759           0.029764                   0.130735  0.041325            0.085748                  0.239050  0.033634  0.024580\n",
            "35    0.034794  0.051759  0.035030              0.053346              0.034547  0.024840  0.034786  0.054479  0.091702           0.029703                   0.130796  0.041336            0.086187                  0.238563  0.033555  0.024576\n",
            "36    0.034777  0.051622  0.034954              0.053234              0.034447  0.025216  0.034994  0.054335  0.091633           0.029643                   0.131089  0.041227            0.086718                  0.238145  0.033464  0.024504\n",
            "37    0.034677  0.051759  0.035853              0.053504              0.034406  0.025425  0.034990  0.054176  0.091960           0.029557                   0.130707  0.041218            0.086428                  0.237393  0.033367  0.024581\n",
            "38    0.034600  0.051849  0.036057              0.053593              0.034382  0.025449  0.034946  0.054049  0.092039           0.029497                   0.130785  0.041301            0.086711                  0.236886  0.033285  0.024573\n",
            "39    0.034519  0.051727  0.035979              0.053477              0.034302  0.025508  0.035161  0.054052  0.091917           0.029450                   0.131482  0.041204            0.086947                  0.236505  0.033214  0.024556\n",
            "40    0.034421  0.051773  0.036363              0.053806              0.034231  0.025655  0.035258  0.053966  0.092511           0.029371                   0.131157  0.041229            0.086679                  0.235790  0.033128  0.024662\n",
            "41    0.034346  0.051809  0.036802              0.053860              0.034199  0.025605  0.035199  0.053858  0.092582           0.029307                   0.131170  0.041282            0.086811                  0.235394  0.033070  0.024708\n",
            "42    0.034283  0.051712  0.036738              0.053817              0.034150  0.025769  0.035303  0.053826  0.092447           0.029290                   0.131531  0.041203            0.087158                  0.235100  0.033004  0.024669\n",
            "43    0.034204  0.051783  0.036982              0.054077              0.034130  0.025876  0.035334  0.053718  0.092924           0.029244                   0.131325  0.041174            0.086960                  0.234561  0.032930  0.024776\n",
            "44    0.034137  0.051824  0.037081              0.054150              0.034065  0.025863  0.035279  0.053631  0.093028           0.029189                   0.131395  0.041184            0.087312                  0.234221  0.032863  0.024778\n",
            "45    0.034080  0.051736  0.037025              0.054068              0.034007  0.025914  0.035421  0.053589  0.092940           0.029147                   0.131889  0.041112            0.087555                  0.233975  0.032806  0.024736\n",
            "46    0.034014  0.051882  0.037305              0.054214              0.033983  0.026090  0.035487  0.053512  0.093231           0.029090                   0.131680  0.041088            0.087375                  0.233497  0.032748  0.024805\n",
            "47    0.033960  0.051926  0.037467              0.054185              0.033950  0.026069  0.035433  0.053470  0.093297           0.029043                   0.131685  0.041136            0.087572                  0.233273  0.032697  0.024837\n",
            "\n",
            "FEVD for EPU_US\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.010336  0.033232  0.002461              0.008925              0.005917  0.006924  0.000644  0.004642  0.014993           0.002576                   0.001673  0.003705            0.000586                  0.001417  0.901968  0.000000\n",
            "1     0.008691  0.026860  0.013922              0.015139              0.008043  0.009868  0.005083  0.011516  0.020168           0.012052                   0.005952  0.007450            0.001088                  0.027874  0.816457  0.009837\n",
            "2     0.010260  0.021176  0.011666              0.013273              0.018931  0.040169  0.014113  0.014173  0.015893           0.010294                   0.043442  0.013177            0.001525                  0.024516  0.721381  0.026010\n",
            "3     0.012125  0.019985  0.023629              0.025816              0.018391  0.037016  0.012934  0.015196  0.018586           0.022939                   0.052549  0.011559            0.011318                  0.049748  0.631157  0.037053\n",
            "4     0.016629  0.019313  0.032912              0.027138              0.022324  0.034577  0.013302  0.019483  0.017329           0.039347                   0.049616  0.015393            0.016815                  0.049613  0.587063  0.039144\n",
            "5     0.050523  0.034011  0.032096              0.024413              0.029253  0.031018  0.012787  0.017572  0.022565           0.045826                   0.046563  0.023071            0.019086                  0.044553  0.528756  0.037906\n",
            "6     0.046240  0.044331  0.029637              0.022531              0.031557  0.029262  0.013716  0.016085  0.043770           0.044228                   0.042683  0.041364            0.017540                  0.049821  0.488217  0.039019\n",
            "7     0.045841  0.044146  0.028339              0.021564              0.031729  0.034156  0.013687  0.018605  0.050848           0.042333                   0.041518  0.045909            0.018498                  0.050031  0.468080  0.044716\n",
            "8     0.047379  0.042982  0.027652              0.027112              0.031479  0.033263  0.013451  0.019289  0.054955           0.041329                   0.043235  0.050781            0.018188                  0.051048  0.454408  0.043448\n",
            "9     0.046595  0.044437  0.028070              0.026907              0.031536  0.032904  0.017029  0.019503  0.063344           0.042062                   0.042228  0.050801            0.017680                  0.049956  0.443839  0.043108\n",
            "10    0.045875  0.048408  0.031052              0.025873              0.031824  0.032702  0.018714  0.020944  0.061971           0.041072                   0.045126  0.049089            0.017513                  0.049180  0.434763  0.045894\n",
            "11    0.044915  0.048316  0.032164              0.025530              0.031512  0.032857  0.018308  0.026107  0.060792           0.040701                   0.051060  0.047985            0.018422                  0.048102  0.428296  0.044933\n",
            "12    0.044275  0.049649  0.031791              0.025184              0.031213  0.035433  0.018753  0.025882  0.060380           0.040189                   0.053603  0.048558            0.018168                  0.048324  0.422753  0.045844\n",
            "13    0.043090  0.050165  0.031009              0.030351              0.032856  0.038938  0.018781  0.025395  0.059866           0.039673                   0.052328  0.051116            0.020787                  0.048817  0.411779  0.045048\n",
            "14    0.043612  0.050078  0.031918              0.030502              0.032686  0.038607  0.018974  0.028955  0.059363           0.039352                   0.052413  0.050733            0.020661                  0.048412  0.408192  0.045541\n",
            "15    0.043765  0.050080  0.031835              0.030402              0.032580  0.039184  0.019105  0.028944  0.059137           0.040300                   0.052373  0.050873            0.020580                  0.048378  0.407081  0.045381\n",
            "16    0.044147  0.049704  0.031463              0.032325              0.032764  0.039154  0.020695  0.028767  0.059511           0.042889                   0.052622  0.051050            0.020324                  0.047776  0.401992  0.044819\n",
            "17    0.045464  0.049036  0.031398              0.033406              0.032629  0.038826  0.024848  0.029422  0.058772           0.043143                   0.054165  0.050657            0.020565                  0.047407  0.395519  0.044743\n",
            "18    0.045004  0.048526  0.032555              0.033258              0.032518  0.038703  0.024994  0.029810  0.061845           0.042685                   0.053794  0.050119            0.021115                  0.047613  0.391432  0.046027\n",
            "19    0.045074  0.048717  0.032762              0.033323              0.032467  0.038646  0.025070  0.029766  0.062232           0.042609                   0.053697  0.050123            0.021084                  0.047546  0.390732  0.046152\n",
            "20    0.045855  0.049161  0.032629              0.033249              0.032336  0.039298  0.025127  0.029639  0.062324           0.042432                   0.053756  0.050092            0.021283                  0.047366  0.389488  0.045965\n",
            "21    0.045806  0.049375  0.033931              0.033634              0.032365  0.039655  0.024993  0.029554  0.062165           0.042705                   0.053411  0.049832            0.021276                  0.047981  0.387624  0.045695\n",
            "22    0.045641  0.049246  0.034668              0.034583              0.032601  0.039512  0.024880  0.029529  0.061886           0.042987                   0.053631  0.049651            0.021552                  0.047811  0.385824  0.045997\n",
            "23    0.045612  0.049372  0.034596              0.034550              0.032549  0.039454  0.025015  0.029468  0.061752           0.042999                   0.053886  0.049802            0.022297                  0.047790  0.384970  0.045889\n",
            "24    0.045760  0.049656  0.034566              0.034923              0.032657  0.039517  0.024957  0.029390  0.061689           0.042873                   0.053792  0.050627            0.022309                  0.047660  0.383816  0.045808\n",
            "25    0.046353  0.050340  0.034427              0.034918              0.032532  0.039542  0.024999  0.029262  0.061751           0.042687                   0.053841  0.051072            0.022798                  0.047469  0.382219  0.045790\n",
            "26    0.046497  0.050665  0.034423              0.034824              0.032437  0.039529  0.024970  0.029176  0.061672           0.042564                   0.054466  0.050923            0.023446                  0.047347  0.381287  0.045772\n",
            "27    0.046441  0.050615  0.034466              0.034929              0.032439  0.039846  0.024960  0.029147  0.061809           0.042599                   0.054402  0.050900            0.023538                  0.047309  0.380840  0.045760\n",
            "28    0.046431  0.050531  0.034565              0.034953              0.032429  0.039779  0.024920  0.029149  0.062198           0.042699                   0.054483  0.050922            0.023717                  0.047312  0.380204  0.045709\n",
            "29    0.046442  0.050458  0.034528              0.034977              0.032387  0.039757  0.024957  0.029254  0.062106           0.042663                   0.054586  0.050926            0.024154                  0.047509  0.379650  0.045646\n",
            "30    0.046511  0.050427  0.034491              0.035040              0.032353  0.039740  0.024951  0.029229  0.062418           0.042641                   0.054757  0.050888            0.024135                  0.047523  0.379243  0.045653\n",
            "31    0.046476  0.050414  0.034456              0.035219              0.032322  0.039699  0.024940  0.029221  0.062687           0.042717                   0.054699  0.050864            0.024354                  0.047480  0.378836  0.045617\n",
            "32    0.046409  0.050344  0.034497              0.035164              0.032290  0.039804  0.025020  0.029233  0.062598           0.042654                   0.054868  0.050792            0.024893                  0.047641  0.378245  0.045550\n",
            "33    0.046354  0.050301  0.034816              0.035226              0.032468  0.039921  0.025185  0.029171  0.062858           0.042674                   0.054837  0.050729            0.024917                  0.047685  0.377386  0.045472\n",
            "34    0.046291  0.050391  0.035084              0.035230              0.032516  0.039885  0.025151  0.029179  0.062978           0.042678                   0.054840  0.050684            0.025192                  0.047620  0.376862  0.045418\n",
            "35    0.046276  0.050321  0.035035              0.035234              0.032473  0.039842  0.025199  0.029222  0.062918           0.042619                   0.055077  0.050627            0.025751                  0.047694  0.376349  0.045363\n",
            "36    0.046227  0.050414  0.035029              0.035320              0.032434  0.039931  0.025269  0.029228  0.063222           0.042566                   0.055143  0.050621            0.025725                  0.047675  0.375888  0.045308\n",
            "37    0.046185  0.050446  0.035169              0.035427              0.032404  0.039928  0.025251  0.029209  0.063371           0.042531                   0.055126  0.050599            0.025875                  0.047632  0.375542  0.045307\n",
            "38    0.046148  0.050398  0.035198              0.035404              0.032379  0.039975  0.025279  0.029191  0.063316           0.042563                   0.055348  0.050551            0.026107                  0.047660  0.375210  0.045274\n",
            "39    0.046099  0.050423  0.035237              0.035495              0.032361  0.040072  0.025282  0.029166  0.063512           0.042645                   0.055358  0.050608            0.026079                  0.047615  0.374769  0.045280\n",
            "40    0.046044  0.050472  0.035448              0.035549              0.032331  0.040029  0.025269  0.029134  0.063603           0.042618                   0.055369  0.050722            0.026244                  0.047597  0.374305  0.045268\n",
            "41    0.045997  0.050440  0.035460              0.035512              0.032297  0.040006  0.025356  0.029116  0.063571           0.042601                   0.055661  0.050687            0.026509                  0.047648  0.373916  0.045222\n",
            "42    0.045957  0.050423  0.035465              0.035593              0.032270  0.040071  0.025410  0.029093  0.063886           0.042590                   0.055684  0.050659            0.026491                  0.047622  0.373590  0.045195\n",
            "43    0.045920  0.050433  0.035577              0.035680              0.032246  0.040043  0.025389  0.029073  0.064027           0.042555                   0.055728  0.050625            0.026607                  0.047625  0.373285  0.045189\n",
            "44    0.045886  0.050397  0.035550              0.035653              0.032223  0.040049  0.025455  0.029091  0.063982           0.042540                   0.055923  0.050589            0.026769                  0.047735  0.373004  0.045155\n",
            "45    0.045851  0.050396  0.035691              0.035694              0.032219  0.040085  0.025505  0.029098  0.064065           0.042517                   0.055982  0.050564            0.026749                  0.047709  0.372717  0.045158\n",
            "46    0.045831  0.050400  0.035813              0.035693              0.032200  0.040064  0.025488  0.029077  0.064109           0.042494                   0.055967  0.050549            0.026928                  0.047729  0.372472  0.045186\n",
            "47    0.045810  0.050370  0.035793              0.035689              0.032182  0.040056  0.025497  0.029075  0.064082           0.042492                   0.056167  0.050519            0.027077                  0.047779  0.372248  0.045163\n",
            "\n",
            "FEVD for EPU_UK\n",
            "        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n",
            "0     0.004516  0.000010  0.007869              0.016955              0.044257  0.001084  0.000026  0.000691  0.029768           0.043574                   0.001273  0.000014            0.004060                  0.001751  0.008983  0.835167\n",
            "1     0.003821  0.004984  0.042726              0.014364              0.036290  0.002626  0.014807  0.005406  0.026089           0.035617                   0.012740  0.005551            0.034720                  0.001881  0.007992  0.750387\n",
            "2     0.011380  0.006064  0.045370              0.013039              0.039722  0.023998  0.016864  0.006456  0.023471           0.038387                   0.015907  0.005334            0.032231                  0.011411  0.026039  0.684326\n",
            "3     0.023327  0.006678  0.043388              0.013048              0.038757  0.023170  0.023021  0.011214  0.031642           0.040248                   0.017487  0.005019            0.030326                  0.023903  0.024923  0.643849\n",
            "4     0.031768  0.006316  0.050588              0.029298              0.037012  0.025327  0.022160  0.010765  0.031639           0.037726                   0.035094  0.004927            0.030412                  0.022370  0.024814  0.599782\n",
            "5     0.030542  0.009983  0.048179              0.038625              0.034834  0.036279  0.023092  0.011448  0.029747           0.034763                   0.032256  0.020629            0.045863                  0.020719  0.031225  0.551814\n",
            "6     0.034016  0.017157  0.048927              0.037627              0.032964  0.039456  0.022380  0.012311  0.041533           0.034088                   0.030946  0.019702            0.046604                  0.020234  0.039896  0.522159\n",
            "7     0.032723  0.025300  0.048922              0.035707              0.035067  0.041599  0.023012  0.025326  0.039705           0.033194                   0.037708  0.018955            0.044273                  0.023563  0.039304  0.495639\n",
            "8     0.032270  0.026550  0.048974              0.039512              0.034330  0.040676  0.023047  0.027142  0.048625           0.033052                   0.042052  0.018506            0.044457                  0.022998  0.038047  0.479762\n",
            "9     0.033306  0.033682  0.048250              0.039879              0.033541  0.040633  0.023627  0.027539  0.051048           0.032277                   0.043861  0.019707            0.043577                  0.023170  0.037122  0.468783\n",
            "10    0.037888  0.033183  0.049072              0.039057              0.032817  0.040951  0.023213  0.028445  0.053791           0.033212                   0.042825  0.020552            0.043244                  0.025897  0.037435  0.458417\n",
            "11    0.040733  0.033100  0.047921              0.039903              0.032789  0.051476  0.022734  0.030499  0.052525           0.032482                   0.041925  0.020070            0.042212                  0.025310  0.036717  0.449603\n",
            "12    0.040530  0.032550  0.047158              0.039553              0.033052  0.051870  0.022800  0.030717  0.053936           0.036043                   0.041299  0.019782            0.041563                  0.024948  0.041022  0.443177\n",
            "13    0.040428  0.033368  0.047426              0.039656              0.032894  0.052570  0.024074  0.032640  0.054992           0.035944                   0.041166  0.019757            0.041016                  0.024625  0.041979  0.437466\n",
            "14    0.040836  0.032911  0.047967              0.039122              0.032474  0.051869  0.024047  0.035239  0.056372           0.036191                   0.040607  0.020309            0.040695                  0.024342  0.043744  0.433276\n",
            "15    0.040905  0.032784  0.047776              0.039912              0.032332  0.051636  0.025279  0.035270  0.056898           0.036568                   0.040365  0.021371            0.040524                  0.024201  0.043486  0.430693\n",
            "16    0.040586  0.032733  0.047828              0.040205              0.032101  0.053111  0.025340  0.035436  0.058531           0.036593                   0.040737  0.021204            0.040397                  0.024027  0.043287  0.427884\n",
            "17    0.040613  0.033386  0.047663              0.040442              0.031998  0.052971  0.025728  0.035445  0.058316           0.036560                   0.040814  0.021144            0.040372                  0.024501  0.043208  0.426839\n",
            "18    0.041742  0.033851  0.047445              0.040752              0.031882  0.052687  0.025755  0.035382  0.058620           0.036536                   0.041183  0.021109            0.040407                  0.025186  0.043101  0.424362\n",
            "19    0.041595  0.034326  0.047239              0.041410              0.031974  0.052837  0.025686  0.035434  0.058640           0.036405                   0.042068  0.021647            0.040634                  0.025054  0.042878  0.422170\n",
            "20    0.041945  0.034419  0.047307              0.042045              0.031897  0.052702  0.025690  0.035482  0.058588           0.036446                   0.042292  0.021629            0.040990                  0.025204  0.042700  0.420663\n",
            "21    0.041947  0.034581  0.047514              0.041937              0.031911  0.052547  0.025890  0.035795  0.058601           0.036301                   0.043566  0.021646            0.040881                  0.025105  0.042711  0.419066\n",
            "22    0.041932  0.034830  0.047692              0.042040              0.031847  0.052472  0.025840  0.035789  0.058473           0.036235                   0.043503  0.021603            0.041391                  0.025584  0.042624  0.418147\n",
            "23    0.042028  0.035135  0.047822              0.041946              0.031783  0.052395  0.026159  0.035737  0.058682           0.036160                   0.043453  0.021724            0.041412                  0.025711  0.042577  0.417276\n",
            "24    0.042448  0.035191  0.047947              0.041917              0.031746  0.052362  0.026193  0.035674  0.058841           0.036231                   0.043445  0.021842            0.041339                  0.025710  0.042553  0.416563\n",
            "25    0.042533  0.035148  0.047854              0.041872              0.031774  0.052461  0.026174  0.035831  0.058710           0.036840                   0.043382  0.021792            0.041410                  0.025817  0.042601  0.415800\n",
            "26    0.042464  0.035439  0.048359              0.041818              0.031749  0.052367  0.026127  0.035812  0.058677           0.037049                   0.043305  0.021866            0.041583                  0.025773  0.042551  0.415060\n",
            "27    0.042409  0.035826  0.048318              0.041832              0.031854  0.052324  0.026095  0.035779  0.058609           0.037013                   0.043351  0.021978            0.041758                  0.025767  0.042503  0.414585\n",
            "28    0.042501  0.035895  0.048300              0.041795              0.031892  0.052279  0.026077  0.035899  0.058702           0.036981                   0.043336  0.021980            0.041830                  0.025818  0.042491  0.414222\n",
            "29    0.042483  0.035872  0.048273              0.042002              0.031900  0.052294  0.026063  0.035877  0.058773           0.036979                   0.043349  0.021974            0.041805                  0.025805  0.042588  0.413962\n",
            "30    0.042459  0.035929  0.048545              0.041975              0.031899  0.052254  0.026085  0.035859  0.058733           0.036950                   0.043338  0.021962            0.041812                  0.025941  0.042606  0.413654\n",
            "31    0.042429  0.035905  0.048520              0.042018              0.031949  0.052216  0.026068  0.035839  0.058691           0.037070                   0.043543  0.021947            0.041827                  0.025969  0.042655  0.413354\n",
            "32    0.042410  0.035892  0.048553              0.042001              0.031940  0.052200  0.026097  0.035892  0.058661           0.037062                   0.043694  0.021934            0.041894                  0.026025  0.042631  0.413115\n",
            "33    0.042416  0.035904  0.048536              0.041981              0.031954  0.052175  0.026080  0.035883  0.058637           0.037093                   0.043839  0.021983            0.041955                  0.026035  0.042619  0.412910\n",
            "34    0.042434  0.035898  0.048549              0.041967              0.031940  0.052179  0.026089  0.035868  0.058673           0.037098                   0.043882  0.021995            0.041940                  0.026032  0.042664  0.412792\n",
            "35    0.042423  0.035889  0.048618              0.042046              0.031925  0.052177  0.026115  0.035855  0.058688           0.037081                   0.043922  0.022002            0.041921                  0.026023  0.042648  0.412668\n",
            "36    0.042418  0.035881  0.048697              0.042047              0.031916  0.052161  0.026107  0.035852  0.058746           0.037080                   0.043910  0.022005            0.041912                  0.026038  0.042635  0.412596\n",
            "37    0.042410  0.035873  0.048701              0.042035              0.031911  0.052144  0.026135  0.035856  0.058728           0.037068                   0.043986  0.021998            0.041941                  0.026033  0.042644  0.412538\n",
            "38    0.042392  0.035859  0.048867              0.042019              0.031906  0.052138  0.026155  0.035845  0.058775           0.037066                   0.044015  0.021995            0.041932                  0.026028  0.042638  0.412368\n",
            "39    0.042377  0.035910  0.048925              0.042019              0.031893  0.052148  0.026175  0.035838  0.058751           0.037126                   0.044097  0.021984            0.041953                  0.026023  0.042617  0.412163\n",
            "40    0.042371  0.035935  0.048920              0.042020              0.031920  0.052147  0.026177  0.035837  0.058763           0.037120                   0.044097  0.021990            0.041948                  0.026021  0.042611  0.412123\n",
            "41    0.042369  0.035978  0.048928              0.042052              0.031921  0.052147  0.026175  0.035833  0.058759           0.037117                   0.044096  0.021989            0.041942                  0.026018  0.042605  0.412070\n",
            "42    0.042370  0.035992  0.048928              0.042052              0.031920  0.052147  0.026176  0.035831  0.058753           0.037112                   0.044093  0.021992            0.041984                  0.026021  0.042601  0.412030\n",
            "43    0.042362  0.035997  0.048936              0.042044              0.031914  0.052138  0.026181  0.035833  0.058740           0.037118                   0.044186  0.021995            0.041996                  0.026016  0.042599  0.411944\n",
            "44    0.042355  0.036002  0.048970              0.042037              0.031910  0.052130  0.026179  0.035831  0.058759           0.037127                   0.044221  0.021991            0.041996                  0.026011  0.042592  0.411886\n",
            "45    0.042346  0.036039  0.048966              0.042030              0.031915  0.052133  0.026173  0.035828  0.058775           0.037118                   0.044290  0.022013            0.041989                  0.026007  0.042586  0.411791\n",
            "46    0.042339  0.036032  0.048961              0.042022              0.031912  0.052141  0.026184  0.035854  0.058774           0.037118                   0.044335  0.022033            0.041988                  0.026016  0.042580  0.411711\n",
            "47    0.042333  0.036032  0.048974              0.042031              0.031910  0.052142  0.026204  0.035850  0.058801           0.037115                   0.044329  0.022030            0.041991                  0.026018  0.042580  0.411662\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# autocorrelation with itself dominates\n",
        "fevd = results.fevd(48)\n",
        "#fevd.summary()\n",
        "fevd_summary = fevd.summary()\n",
        "fevd_summary\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fevd.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LRr60homRLWe",
        "outputId": "fa4f9375-c5e5-482d-8b3f-4d3144c4d939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c9ksV2wpInU4"
      },
      "outputs": [],
      "source": [
        "#print latex(fevd_summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BTRdquA5ikiu"
      },
      "outputs": [],
      "source": [
        "results.fevd(48).plot(figsize=(30,30));\n",
        "print(\"FEVD\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "16-EDIlbFBtR"
      },
      "outputs": [],
      "source": [
        "import statsmodels.api as sm\n",
        "results_summary = results.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jxIarrFHnpL1"
      },
      "outputs": [],
      "source": [
        "#!pip install rpy2==3.5.1\n",
        "#%load_ext rpy2.ipython"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sq_1vdgJrNfk"
      },
      "outputs": [],
      "source": [
        "#%%R\n",
        "\n",
        "#plot(results.fevd(20), col=2:15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TcEitqPk9e9B"
      },
      "outputs": [],
      "source": [
        "#gc = results.test_causality('x', ['x', 'y'], kind='f')\n",
        "#gc.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiwnPK7r9lxu"
      },
      "outputs": [],
      "source": [
        "# forecast\n",
        "results.plot_forecast(10);\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}